Research waste in randomized controlled trials of endometrial cancer: a 20-year cross-sectional study

Research waste in randomized controlled trials of endometrial cancer: a 20-year cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Research waste in randomized controlled trials of endometrial cancer: a 20-year cross-sectional study Jin-Peng Yi, Mu XU, Li-Ying Zhong, Peng-ming Sun, Yi-Fang Dai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9250716/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Randomized controlled trials (RCTs) provide high-level evidence for the management of endometrial cancer. However, research waste—including non-publication, inadequate reporting, and avoidable design flaws—remains a major challenge in clinical research. To our knowledge, no previous study has systematically evaluated research waste in RCTs of endometrial cancer. This study aimed to assess the characteristics of RCTs and quantify the extent of research waste over the past 20 years. Methods We conducted a cross-sectional analysis of RCTs registered on ClinicalTrials.gov between January 2004 and January 2024. Publication status was identified through PubMed and Scopus. Reporting quality was evaluated using the CONSORT checklist, and methodological quality was assessed using the Cochrane risk-of-bias tool. Research waste was defined as the presence of at least one of the following: non-publication, inadequate reporting, or avoidable design limitations. Logistic regression analyses were performed to identify factors associated with research waste. Results A total of 144 RCTs were included, of which 57 (39.6%) were published. Overall, 129 trials (89.6%) exhibited at least one feature of research waste. Among published trials, 52.6% had adequate reporting, and 43.9% showed avoidable design flaws. Trials with double- or multi-blind designs were more likely to be adequately reported and had a lower risk of research waste. Multicenter trials and those conducted in North America were more likely to be cited in clinical guidelines. Prospective data reuse was rare (1.8%). In multivariate analysis, blinding design was independently associated with reduced research waste. Conclusions Research waste is highly prevalent in RCTs of endometrial cancer. Improving trial design, promoting multicenter collaboration, and strengthening reporting standards may help reduce research waste and enhance the translation of evidence into clinical practice. Figures Figure 1 Figure 2 Figure 3 Introduction Endometrial cancer is a common malignancy among women experiencing peri- and postmenopause [ 1 ] , with a global age-standardized incidence rate of 8.7 per 100,000, which is increasing annually [ 2 ][ 3 ] . In terms of visits, diagnosis, treatment, and follow-up monitoring, endometrial cancer imposes a significant economic burden [ 4 ][ 5 ] . To improve the prognosis of endometrial cancer, an increasing number of randomized clinical trials (RCTs) are exploring new and potentially more effective treatments. Over the past 20 years, these RCTs have provided a high level of medical evidence to support the clinical diagnosis and treatment of endometrial cancer, which has significantly improved the quality of life of affected patients. Despite the high level of evidence provided by these RCTs and their significant contribution to advancing treatment, research waste has inevitably become a major challenge in evidence-based medicine. This implies that wasteful RCTs not only consume resources but also expose participants to unnecessary risks [ 6 ] . This waste can be generated at any stage of the study cycle. Research waste may ultimately preclude clinical practice guidelines in relevant fields from adopting results. To our knowledge, no previous study has systematically evaluated research waste in randomized controlled trials of endometrial cancer. Therefore, this study aimed to analyze the characteristics of RCTs conducted over the past 20 years and to quantify the extent of research waste, including non-publication, inadequate reporting, and avoidable design flaws. In addition, we examined whether published RCTs were cited in clinical guidelines and whether prospective data were reused, in order to identify potential targets for improving research quality and reducing waste. This cross-sectional study has been reported in line with the STROCSS guidelines [ 11 ] .This study provides the first comprehensive assessment of research waste in endometrial cancer RCTs. Methods This study was based on a retrospective analysis of publicly registered randomized controlled trials (RCTs) and did not involve patient recruitment and the collection of sensitive information, so the routine ethical approval process and the signing of informed consent forms could be waived. This study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. Design and Data Sources The data for this study originated from ClinicalTrials.gov, the authoritative clinical trial registry [ 7 ] . Specifically, we used the keywords "Cancer OR Endometrial neoplasm OR Endometrial Carcinoma OR Endometrial Adenocarcinomas" on the specified date (i.e., April 2025). A comprehensive search of the ClinicalTrials.gov database was conducted. The inclusion criteria were set as randomized controlled trials (RCTs) registered in the database from January 1, 2004, to January 1, 2024, with a careful review of the trial titles and abstracts to determine whether they met the study conditions. Phase I and II trials were excluded as their primary objective was to preliminarily validate efficacy and safety, and they might not follow the regular publication process. In addition, non-randomized trials, programs not associated with endometrial cancer, duplicate studies, and those whose RCTs were completed later than January 1, 2024, and were not published were excluded. This time frame was chosen to ensure that research teams had ample time to complete the paper writing, submission, peer review, and editing processes, thereby improving the quality and completeness of published results [ 6 ] . Independent researchers conducted the assessment and resolved the differences through consensus. In this study, all included RCTs were ranked in descending order according to sample size, and the top 25% were defined as large-sample RCTs. Considering that North America and Europe have a high incidence of endometrial cancer and a substantial disease burden [ 8 ] , we further classified RCTs based on the Healthcare Access and Quality (HAQ) Index of the country where the principal investigator was located [ 9 ] . Trials were categorized into regions with a HAQ index ≥ 90 and < 90. In addition, countries were classified according to the World Bank income classification [ 10 ] , and comparisons were made between high-income and non-high-income settings. Status of Publication Publication status was determined by searching PubMed and Scopus using the ClinicalTrials.gov identifier (NCT number), the principal investigator (PI) name, and relevant keywords. If no corresponding publication was identified, the PI was contacted for confirmation. If no response was received, the trial was considered unpublished [ 6 ] . If a full-text manuscript (either in print or online) is found in a peer-reviewed journal, the trial is considered to have been published. The last search was conducted on August 30, 2025. Reporting Adequacy Assessment To evaluate the Reporting completeness of each article, we conducted a detailed assessment of each report in accordance with the Consolidated Standards of Reporting Trials (CONSORT) guidelines. Considering the different characteristics of drug intervention and non-drug intervention, checklists containing 37 and 40 checkpoints were designed, respectively. One member of the research team is responsible for collecting all relevant publications and their supplementary materials. By using the PDF to Word feature of Adobe Acrobat Pro PDF software (Adobe Systems Inc.), he removed the author and journal identifiers in the document and adjusted the text layout to minimize potential biases. Subsequently, the printed documents were thoroughly reviewed and scored for each literature by two independent researchers in accordance with the CONSORT 2010 checklist. After the evaluation of every three articles is completed, the two will discuss any inconsistent opinions until a consensus is reached [ 12 ] . If an article on a randomized controlled trial (RCT) involving drug intervention reports at least 27 items, or if an RCT article involving non-drug intervention covers at least 30 items, we consider its reporting to have met the adequate standard. This threshold is determined based on the median meta-analysis of reporting compliance and is also a pre-planned assessment strategy [ 6 ] . Design Flaw Assessment Two independent researchers used Cochrane tools [ 13 ] to review the masked manuscripts and assess the risks of selection bias, performance bias, detection bias, attrition bias, reporting bias, and other biases. The risk of bias for each project is classified as low risk, unclear, or high risk. After reviewing every three manuscripts, the researchers will discuss the existing differences and reach a consensus. In statistical analysis, projects with unclear risk of bias are regarded as high-risk. This is because a vague description of the key methods will affect the judgment of the RCT's ability to provide information. In addition, the researchers also evaluated whether there were relevant systematic reviews or whether it was necessary to conduct systematic reviews in the new environment. Only when the review is cited throughout the entire manuscript, and it is believed that the review can demonstrate the necessity of conducting an RCT, is it regarded as having a systematic review. If one of the aforementioned biases occurs in the article or if the relevant systematic review is not cited, it is regarded as having avoidable design flaws. Whether Referenced in Guidelines and Reuse of Prospective Data Excluding RCTs published after January 1, 2024, for the remaining publicly released randomized controlled trials (RCTs), our initial step is to track all research literature that cites this RCT in the academic search engine Google Scholar [ 14 ] . Next, two unrelated researchers personally reviewed this literature, and their task was to identify whether there were treatment or practice guidelines among them. In addition, we also evaluated whether these follow-up studies utilized the prospective data from the original RCTs for post hoc analysis, that is, whether the original RCT data were reanalyzed to obtain results other than the pre-defined primary and secondary study endpoints [ 15 – 17 ] Outcome The primary outcome of the study is to analyze the characteristics of clinical research trials conducted over the past two decades and to deeply explore the so-called "research waste" phenomenon, which includes unpublished research, insufficient reporting information, and avoidable design flaws. In addition, this study also explored whether published RCTs were cited by guidelines and whether prospective data were reused, as published RCTs are a prerequisite for evaluating the adequacy of reporting and design flaws. All RCTs completed after January 1, 2024, and not published were excluded from the analysis. Statistical Analysis Use χ2 or Fisher's test (if the sample size is less than 5) to compare the differences in categorical variables between groups [ 18 ] . Simple and multivariate logistic regression models are used to identify independent risk factors associated with research waste. Variables with P < 0.05 in the simple analysis were subsequently included in the multivariate analysis. All Statistical analyses were conducted using SPSS Statistical software 18.0 for Windows (IBM) and R Statistical software 4.0.2 (R Project for Statistical Computing). A P value less than 0.05 was considered statistically significant, and all tests were two-sided tests. The data analysis was conducted in July 2025. Results RCT development on a global scale A literature search for relevant studies, published between 2004 and 2024, retrieved 263 RCTs that fulfilled the inclusion criteria. After excluding 94 non-RCTs, 1 pediatric science trial, 21 trials not related to endometrial cancer, and 3 duplicate trials, 144 RCTs were included in the present analysis, among which 23.6% addressed drugs (n = 34)(Fig. 1). In terms of regional distribution, 79.8% of the RCTs had their primary researchers situated in North America or Europe, with 64 trials in North America (44.4%) and 51 in Europe (35.4%). Single-center RCTs outnumbered multi-center trials (n = 84 [58.3%] versus [vs.] n = 55 [38.1%], respectively). RCTs with sample sizes > 350 were defined as “large”. Among the included studies, 140 (97.2%) were internally funded. Other relevant information is summarized in Table 1 . Table 1 Characteristics of included randomized controlled trials Characteristic RCTs,NO.(%)N = 144 Time of registration 2004 ~ 2008 11(7.6) 2009 ~ 2013 23(16.0) 2014 ~ 2018 35(24.3) 2019 ~ 2024 75(52.1) Intervention Drug 34(23.6) Procedure 38(26.4) Device 14(9.7) Other 58(40.3) Primary Purpose Treatment 69(47.9) Prevent 16(11.1) Support care 38(26.3) Other 21(14.6) Intervention model Parallel 132(91.6) Non- Parallel 12(8.4) Arm 2 124(86.1) ≥ 3 17(11.9) Missing 3(2.0) Blinding None or open label 87(60.4) Single 26(18.0) Double and more 31(21.5) MISS 1(0.1) Recruitment Monocentric 84(58.3) Multicenter Missing 55(38.1) 5(3.4) Funder Type None or departmental 140(97.2) Industry or other external 4(2.8) Region of PI North America 64(44.4) Europe 51(35.4) other 23(16.0) Miss 6(4.2) Region of PI HAQ<90 122(84.7) HAQ ≥ 90 16(11.1) MISS 6(4.2) Region of PI High Income 121(84.0) Non-high income 17(11.8) MISS 6(4.2) The number of RCTs addressing endometrial cancer has been increasing annually, reaching a peak between 2019 and 2024, with a total of 75 trials (52.1%) compared with only 11 (7.6%) from 2004 to 2008. From 2003 to 2024, the number of published RCTs across different categories has demonstrated significant fluctuations (Figure S1 ). The number of RCTs for other and surgical intervention types occupied a larger share, and there were more RCTs addressing surgical and drug intervention than those for equipment types, with the number of RCTs in the equipment intervention category the lowest (Figure S2). Overview of research waste Among the 144 included randomized controlled trials (RCTs), only 57 (39.6%) were published. Among published trials, 30 (52.6%) achieved adequate reporting according to the CONSORT checklist, while 25 (43.9%) exhibited avoidable design flaws. Overall, 129 trials (89.6%) had at least one feature of research waste. A summary of these findings is presented in Fig. 2. Non-publication Overall, 57 registered RCTs (39.6%) were published in peer-reviewed journals, and their full texts were available for review, 87 (60.4%) had not yet been published. Compared with published RCTs, the unpublished RCTs may be related to the procedure (21 trials [38.89%] vs. 16trials [21.92%]; P = 0.017), and are more likely to come from single-center recruitment (31trials [57.41%] vs. 16trials [21.92%]; P = 0.034), and have a smaller sample size (58trials [90.63%] vs. 53trials [72.6%]; P = 0.009). Additionally, published RCTs were more likely to be from high-income countries and those with a high sociodemographic index (66trials [90.41%] vs 45trials [83.3%]; P = 0.031) (Table S1 ). In multivariate analysis, RCTs addressing drug intervention for endometrial cancer were more likely to be published (odds ratio [OR] 0.330 [95% confidence interval (CI) 0.136 0.801]; P = 0.014) (Table S2). Reporting Adequacy scores for the 57 published RCTs were evaluated according to the Consolidated Standards of Reporting Trials (CONSORT) checklist. Thirty (52.6%) RCTs were determined to have adequate reporting. These well-reported RCTs were more likely to adopt double- or even multi-blind designs (11 [36.7%] vs. 2 [7.4%]; P = 0.025) (Table S3). The most significant deficiencies in 20 reports addressing drug intervention were the lack of information regarding the availability of the trial protocol (present in 10% of the RCTs) and the description of the randomization method (35.0%). The most common deficiencies in 37 reports unrelated to drug intervention were the reporting of information regarding the availability of the trial protocol (present in 35.1% of RCTs), details of blinding (32.4%), and details for evaluating or strengthening the compliance of providers with the protocol (51.3%) (Table S4). Design limitations Of the 57 published RCTs, 14 (24.6%) did not cite systematic reviews in the main text and 25 (43.9%) had ≥ 1 feature(s) indicating a high or unclear risk of bias. The most common factors associated with the risk of bias were deviations from established interventions (15 trials [26.3%]) and outcome measures (13 trials [22.8%]) (Fig. 3). Considering these factors, 25 (43.9%) RCTs were determined to have avoidable design flaws. Compared with these trials, RCTs with unavoidable design flaws were more likely to have double- or even multi-blind designs (11 [34.4%] vs. 2 [8.0%]; P = 0.032) (Table S5). Research Waste Considering the combination of publication status, adequate reporting, and avoidable design flaws, 129 (89.6%) of the 144 RCTs had ≥ 1 characteristic(s) of research waste. These RCTs were more likely to have no or open-label blinding than the 15 no-research waste RCTs (82 [63.6%] vs 5 [33.3%]; P = 0.002). In addition, RCTs with no research waste were more likely to be drug related (8 [53.3%] vs. 26 [20.2%]; P = 0.030) (Table 2 ). Table 2 Comparison of trial characteristics according to the presence of research waste Characteristic Without research waste, No. (%) With research waste, No. (%) Total, No. (%) P Funder Type None or departmental 15(100.0) 125(96.9) 140(97.2) 0.489 Industry or other external 0(0.0) 4(3.1) 4(2.8) Intervention Procedure 4(26.7) 34(26.4) 38(26.4) 0.030* Drug 8(53.3) 26(20.2) 34(23.6) Device 1(6.7) 13(10.1) 14(9.7) Other 2(13.3) 56(43.4) 58(40.3) Recruitment Single centre 9(60.0) 75(58.1) 84(58.3) 0.739 Multicenter 6(40.0) 49(38.0) 55(38.2) Missing 0(0.0) 5(3.9) 5(3.5) Arm 2 12(92.3) 112(85.5) 124(86.1) 0.753 ≥ 3 1(7.7) 16(12.2) 17(11.8) Missing 0(0.0) 3(2.3) 3(2.1) Intervention modle Parallel 15(100.0) 117(90.7) 132(91.7) 0.459 Non-Parallel 0(0.0) 12(9.3) 12(8.3) Blinding None or open label 5(33.3) 82(63.6) 87(60.4) 0.002** Single 1(6.7) 25(19.4) 26(18.1) Double or more 9(60.0) 22(17.1) 31(21.5) Primary purpose Treatment 8(53.3) 61(47.3) 69(47.9) 0.657 Other 7(46.7) 68(52.7) 75(52.1) No. of participants < 350 12(80.0) 105(81.4) 117(81.3) 1.000 ≥ 350 3(20.0) 24(18.6) 27(18.8) PI region < 90 14(93.3) 114(88.4) 128(88.9) 0.885 ≥ 90 1(6.7) 15(11.6) 16(11.1) PI region North America 8(53.3) 56(43.4) 64(44.4) 0.451 Europe 3(20.0) 46(35.7) 49(34.0) Other 4(26.7) 27(20.9) 31(21.5) PI region Non-high SDI 3(20.0) 25(19.4) 28(19.4) 1.000 High SDI 12(80.0) 104(80.6) 116(80.6) PI region High income 12(80.0) 104(80.6) 116(80.6) 1.000 Non-high income 3(20.0) 25(19.) 28(19.4) Time of registration 2004 ~ 2013.06 2(13.3) 29(22.5) 31(21.5) 0.628 2013.06 ~ 2024 13(86.7) 100(77.5) 113(78.5) * p < 0.05 ** p < 0.01 In further analyses, single-blind and multi-blind designs were associated with a lower odds of study waste (OR, 0.291 [95% CI 0.094–0.9060]; P = 0.033) (Table 3 ). Table 3 Multivariable logistic regression analysis of factors associated with research waste Univariate analysis Multivariate analysis OR(95%CI) P value OR(95%CI) P value Time of registration 2003 ~ 2013.06 1 2013.06 ~ 2023 0.531(0.113 ~ 2.487) 0.421 Primary purpose Treatment 1 Other 1.274(0.436 ~ 3.720) 0.658 Intervention Non-pharmacological-related 1 Pharmacological-related 0.221(0.073 ~ 0.665) 0.007** 0.767(0.220 ~ 2.673) 0.677 Arm 2 1 3 1.714(0.209 ~ 14.086) 0.616 Blinding None or open label 1 Single or more 0.287(0.092 ~ 0.889) 0.030* 0.291(0.094 ~ 0.906) 0.033* Recruitment Single centre 1 Multicenter 0.980(0.328 ~ 2.926) 0.971 No. of participants < 350 1 ≥ 350 0.914(0.239 ~ 3.494) 0.896 PI region Non-high SDI 1 High SDI 1.040(0.273 ~ 3.965) 0.954 PI region High income 1 Non-high income 1.040(0.273 ~ 3.965) 0.954 PI region < 90 1 ≥ 90 1.842(0.226 ~ 15.027) 0.568 PI region North America 1 Other 1.490(0.510 ~ 4.354) 0.466 * p < 0.05 ** p < 0.01 Referenced in Guidelines and the Reuse of Prospective Data Two RCTs published within 1 year of 2023 were excluded, with 15 trials (26.3%) cited in the corresponding guidelines. In particular, multicenter trials (12 [80.0%] vs. 14 [33.3%]; P = 0.01) were more likely to be sourced from North American countries (11 [73.3%] vs. 20 [47.6%]; P = 0.025) and to use treatment as the primary objective of the trial (n = 12 [80.0%] vs. n = 3 [20.0%]; P = 0.025) (Table S6). In further analyses, multicenter RCTs were more likely to be cited in guidelines (OR 0.158 [95% CI] 0.037–0.679; P = 0.013) (Table S7). Finally, prospective data from only 1 RCT (1.8%) were reused, although there were no significant differences between the groups (Table S8). Discussion For the first time, this cross-sectional study analyzed the characteristics of 144 RCTs investigating endometrial cancer over the past 20 years and found a high level of research waste, with 89.6% of the RCTs having ≥ 1 features of study waste. Fifty-seven (39.6%) RCTs were published and were available for full-text review, of which 25 (17.3%) were judged to have avoidable design flaws. Thirty RCTs (20.8%) were adequately reported. In addition, RCTs published in the most recent year (i.e.,2024) were excluded and 15 trials (10.4%) were cited in the corresponding guidelines. Prospective data from 1 RCT (0.7%) were reused. In further analyses, single- and multi-blind designs were associated with a lower odds of study waste. This study found a significant upward trend in the number of patients with endometrial cancer and RCTs over the past 2 decades. The number of RCTs conducted between 2019 and 2024 (n = 75 [52.1%]) increased significantly compared with that from 2004 to 2008 (n = 11 [7.6%]), which is consistent with the continued upward trend in the incidence of endometrial cancer worldwide. With the increasing disease burden, the need to explore new treatment methods has become increasingly urgent. The increase in the number of RCTs reflects the positive response of the scientific research community to improving the prognosis of affected patients and provides potential evidence supporting the optimization of clinical diagnosis and treatment strategies. In terms of regional distribution, the principal investigators of 79.8% of the RCTs were situated in North America and Europe. This distribution is closely associated with the allocation of medical resources, the intensity of scientific research investment, and the Healthcare Access and Quality (HAQ) index. Regions with high HAQ usually have a better scientific research infrastructure, sufficient financial support, and a mature collaboration network, thus making it easier to conduct RCTs. As a common gynecological malignancy, the diagnosis and treatment of endometrial cancer requires multidisciplinary collaboration, including surgery, drugs, and other interventions, which is highly consistent with the characteristics of surgical disease diagnosis and treatment. Both rely on RCTs to verify the effectiveness of interventions; however, both encounter the serious problem of research waste. Our analysis of 144 endometrial cancer RCTs worldwide over the past 20 years revealed that 89.6% had ≥ 1 feature(s) of study waste (e.g. unpublished, inadequate reporting, or avoidable design flaws), and their core deficiencies highly overlapped with the common problems of surgical RCTs. These issues limit the effective translation of evidence into clinical practice and hinder progress in endometrial cancer research. There is a significant imbalance between the waste of drugs and surgical interventions in research investigating endometrial cancer. Among the 144 RCTs addressing endometrial cancer, only 23.6% (n = 34) were drug intervention studies, while nearly 80% were surgery and other non-drug intervention studies. However, multivariate analysis confirmed that the publication probability of a drug-intervention RCT was significantly higher, and 53.3% (8/15) of the RCTs without research waste were related to drugs, which was much higher than the proportion of drug interventions in the overall number of RCTs (20.2% [26/129]) (P = 0.030). Among the unpublished endometrial cancer RCTs, 38.89% (21/54) were related to procedural interventions such as surgery, which was significantly higher than the 21.92% (16/73) of the published RCTs (P = 0.017). The unpublished RCTs preferred a single-center design (57.41% vs. 21.92%; P = 0.034) and smaller sample size (90.63% vs. 72.6%; P = 0.009). This characteristic of “easy publication of drug research and high waste of surgical research” was more prominent among surgical RCTs. In a study investigating surgical RCTs, Chapman et al [ 3 ] reported that 85.2% of surgical RCTs exhibited research waste. Due to the high design complexity and difficulty in standardization, the unpublished rate of surgical RCTs (approximately 62%) was significantly higher than that for adjuvant drug therapy RCTs (approximately 38%). The analysis of RCTs in the field of gastric cancer also revealed that adequate reporting rate of non-drug interventions (including surgery) RCTs (52.9%) was significantly lower than that for RCTs (87.5%), and 77.8% of surgery-related RCTs had avoidable design defects [ 19 ] . Surgical intervention studies, both in endometrial cancer and in surgical fields (such as those for gastric cancer and gastrointestinal surgery), are research areas exhibiting the most waste due to insufficient resource allocation and difficulties in design and implementation. This intervention imbalance is essentially a common problem of “unreasonable allocation of resources and failure to use resources where they can be used to make a difference” encountered by the 2 types of research. The vagueness of details in surgical RCTs addressing endometrial cancer coincides with common problems in surgical RCTs. According to the CONSORT guidelines, only 32.4% of the 37 non-pharmacological (including surgery) endometrial cancer RCTs clearly described the details of blinding, and 51.3% did not describe specific measures to “evaluate or enhance provider adherence to the protocol”. However, the missing rates of key details, such as surgical technical standards (e.g., incision location for laparoscopic surgery and, the extent of lymph node dissection) and intraoperative adjustment strategies, were higher. Simultaneously, 22.8% (13/57) of the published RCTs had the problem of “non-standard outcome measurement”. The physical characteristics of surgery make “complete blinding” virtually impossible, which constitutes an inherent source of bias in both surgical procedures and surgical RCTs for endometrial cancer. Among RCTs addressing endometrial cancer RCTs, 63.6% (82/129) used a non-blinded or open-label design, which was significantly higher than that for RCTs with no research waste (33.3% [5/15]) (P = 0.002). Multivariate analysis further confirmed that a single- or multi-blind design was associated with a lower risk for research waste (OR = 0.291 [95% CI 0.094–0.906]; P = 0.033) because patients were aware of the group assignment by the type of surgical incision (e.g., laparoscopic vs. open surgery), and surgeons were aware by the surgical method. In this study, 43.9% (25/57) of published RCTs had a “high or unclear risk of bias”. In response to these common problems, feasible strategies have been explored in the field of surgery to, provide a reference for research investigating endometrial cancer. In terms of intervention imbalance, the field of gastric cancer has been encouraged to promote multicenter collaboration and integrate single-center surgical RCTs into multicenter studies [ 18 ] . Research in other fields has highlighted the importance of optimizing resource allocation and promoting multicenter collaboration to improve the quality of randomized trials [ 20 ] . Similar strategies could be applied in endometrial cancer research to enhance study design and reduce research waste. Research investigating endometrial cancer can refer to this concept by establishing a multicenter collaboration platform with gynecological cancer centers, and add a “surgical RCT special” in the allocation of scientific research funds to give priority to research investigating laparoscopic, robot-assisted, and other technologies. In terms of standardizing reporting, the gastric cancer and GERD domains should advocate for reporting adequacy of non-drug intervention RCTs to be assessed using a 40-point checklist designed according to the CONSORT guidelines. In addition, special reporting standards should be developed to describe the details of surgical incision location and extent of lymph node dissection in the text or supplementary materials, and to unify the diagnostic criteria for postoperative complications (such as vaginal stump bleeding). In the process of bias control, based on the Cochrane Collaboration tool to assess the risk of bias, studies investigating endometrial cancer can also use third-party assessment of postoperative pain, quality of life, and other outcomes to reduce bias in RCTs that cannot be blinded, such as those for laparoscopic or open surgeries. Limitations In the analysis of research waste [ 12 ] , first of all, quantifying research waste is also a complicated task. This waste is by no means limited to the 3 elements defined in this study. Categories such as "duplicate studies" and "data not shared" may underestimate the actual extent of waste. Second, the various endpoints were collected manually, which may be related to measurement error. However, we minimized measurement error by having two independent investigators review each RCT and resolve disagreements through discussion. Third, although ClinicalTrials.gov is a comprehensive clinical trial registry, accounting for more than 80% of all clinical studies on the WHO portal [ 21 ] , the World Health Organization also recognizes other registries in specific countries and regions [ 22 ] , and RCTs from these registries were not included in this study. For example, studies from other databases (such as the Chinese Clinical Trial Registry) were omitted, resulting in underrepresentation globally. Fourth, using "the location of the principal investigator" to represent the study area may not correspond to the actual implementation site (such as transnational cooperative research), which may lead to bias in the analysis of regional distribution. Conclusion In conclusion, research waste is highly prevalent in randomized controlled trials of endometrial cancer. Significant deficiencies remain in publication, reporting, and study design. Targeted efforts to improve trial methodology, enhance reporting quality, and promote multicenter collaboration are urgently needed to reduce research waste and improve the efficiency of evidence translation. Declarations Ethical approval This study was a retrospective analysis based on publicly registered randomized controlled trials (RCTs), which did not involve patient recruitment and collection of sensitive information, the routine ethical approval process and the signing of the informed consent form could be exempted. Consent This study was a retrospective analysis based on publicly registered randomized controlled trials (RCTs), which did not involve patient recruitment and collection of sensitive information, the routine ethical approval process and the signing of the informed consent form could be exempted. Conflicts of interest disclosure The authors declare no competing interests. Research registration unique identifying number((UIN) Not applicable. Assistance with the study We thank all contributors to this study, including nurses, pathologists, postgraduate trainees, statisticians, reviewers, and editors. Author Contribution Jin-Peng Yi, and Mu Xu contributed equally to this work and should be considered co-first authors.J.P.Y. and M.X. drafted the main manuscript text and prepared the figures and tables. J.P.Y. and M.X. performed the literature search, data extraction, and quality assessment (including CONSORT and Risk of Bias evaluations).Y.F.D. and P.M.S. conceptualized and designed the study. L.Y.Z. assisted in data curation and statistical analysis. Y.F.D. and P.M.S. supervised the project and critically revised the manuscript for important intellectual content.All authors reviewed and approved the final manuscript. Data Availability All data generated or analyzed during this study are included in this published article. Further details are available from the corresponding author upon reasonable request. References Crosbie EJ, Kitson SJ, McAlpine JN, Mukhopadhyay A, Powell ME, Singh N. Endometrial cancer. Lancet. 2022;399(10333):1412–28. 10.1016/S0140-6736(22)00323-3 . Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin. 2021;71(3):209–49. 10.3322/caac.21660 . Gu B, Shang X, Yan M, et al. Variations in incidence and mortality rates of endometrial cancer at the global, regional, and national levels, 1990–2019. Gynecol Oncol. 2021;161(2):573–80. Yabroff KR, Lund J, Kepka D, Mariotto A. Economic burden of cancer in the United States: estimates, projections, and future research. Cancer Epidemiol Biomarkers Prev. 2011;20(10):2006–14. 10.1158/1055-9965.EPI-11-0650 . Warring SK, Borah B, Moriarty J, et al. The cost of diagnosing endometrial cancer: Quantifying the healthcare cost of an abnormal uterine bleeding workup. Gynecol Oncol. 2022;164(1):93–7. 10.1016/j.ygyno.2021.10.079 . Chapman SJ, Aldaffaa M, Downey CL, Jayne DG. Research waste in surgical randomized controlled trials. Br J Surg. 2019;106(11):1464–71. 10.1002/bjs.11266 . US National Library of Medicine. ClinicalTrials.gov. Accessed April 1. 2024. https://clinicaltrials.gov/ Colombo N, Preti E, Landoni F, et al. Endometrial cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2011;22(Suppl 6):vi35–9. 10.1093/annonc/mdr374 . GBD 2015 Healthcare Access and Quality Collaborators. Disease Study 2015 Lancet. 2017;390(10091):231–66. 10.1016/S0140-6736(17)30818-8 . Electronic address: [email protected] ; GBD 2015 Healthcare Access and Quality Collaborators. Healthcare Access and Quality Index based on mortality from causes amenable to personal health care in 195 countries and territories, 1990–2015: a novel analysis from the Global Burden of. World Bank.World Bank country and lending groups. Accessed May 15. 2024. https://datahelpdesk.worldbank.org/knowledgebase/articles/906519-world-bank-country-and-lendinggroups Agha RA, Mathew G, Rashid R, Kerwan A, Al-Jabir A, Sohrabi C, Franchi T, Nicola M, Agha M. Revised Strengthening the reporting of cohort, cross-sectional and case-control studies in surgery (STROCSS) Guideline: An update for the age of Artificial Intelligence. Premier J Sci 2025:10100081. Lu J, Xu BB, Shen LL, et al. Characteristics and Research Waste Among Randomized Clinical Trials in Gastric Cancer. JAMA Netw Open. 2021;4(9):e2124760. 10.1001/jamanetworkopen 2021.24760 . Published 2021 Sep 1. Flemyng E, Moore TH, Boutron I, et al. Using Risk of Bias 2 to assess results from randomised controlled trials: guidance from Cochrane. BMJ Evid Based Med. 2023;28(4):260–6. 10.1136/bmjebm-2022-112102 . Google G, Scholar. Accessed June 1, 2024. https://scholar.google.com/ Jari M, Shiari R, Salehpour O, Rahmani K. Epidemiological and advanced therapeutic approaches to treatment of uveitis in pediatric rheumatic diseases. A systematic review and meta-analysis. Orphanet J Rare Dis. 2020;15(1):41. 10.1186/s13023-020-1324-x . Yang R, Zhu Y, Xu M, Tao Y, Cong W, Cai J. Intensive blood pressure lowering and the risk of new-onset diabetes in patients with hypertension: a post-hoc analysis of the STEP randomized trial. Eur J Prev Cardiol. 2023;30(10):988–95. 10.1093/eurjpc/zwad105 . Granholm A, Alhazzani W, Derde LPG, et al. Randomised clinical trials in critical care: past, present and future. Intensive Care Med. 2022;48(2):164–78. 10.1007/s00134-021-06587-9 . Braga A, Paiva G, Ghorani E, et al. Predictors for single-agent resistance in FIGO score 5 or 6 gestational trophoblastic neoplasia: a multicentre, retrospective, cohort study. Lancet Oncol. 2021;22(8):1188–98. 10.1016/S1470-2045(21)00262-X . Lu J, Xu BB, Shen LL, et al. Characteristics and Research Waste Among Randomized Clinical Trials in Gastric Cancer. JAMA Netw Open. 2021;4(9):e2124760. 10.1001/jamanetworkopen 2021.24760 . Published 2021 Sep 1. Lin B, Guo XJ, Jiang YM, et al. Characterization changes and research waste in randomized controlled trials of global gastroesophageal reflux disease and hiatus hernia over the past 20 years. Int J Surg. 2025;111(3):2358–75. 10.1097/JS9.0000000000002227 . Published 2025 Mar 1. Minen MT, Reichel JF, Pemmireddy P, Loder E, Torous J. Characteristics of Neuropsychiatric Mobile Health Trials: Cross-Sectional Analysis of Studies Registered on ClinicalTrials.gov. JMIR Mhealth Uhealth. 2020;8(8):e16180. 10.2196/16180 . Published 2020 Aug 4. Gresham G, Meinert JL, Gresham AG, Meinert CL. Assessment of Trends in the Design, Accrual, and Completion of Trials Registered in ClinicalTrials.gov by Sponsor Type. JAMA Netw Open. 2020;3(8):2000–19. 10.1001/jamanetworkopen 2020.14682 . Published 2020 Aug 3. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9250716","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":618321016,"identity":"1c12b022-e139-47b8-bfaa-40c6f049ae2f","order_by":0,"name":"Jin-Peng Yi","email":"","orcid":"","institution":"Fujian Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jin-Peng","middleName":"","lastName":"Yi","suffix":""},{"id":618321019,"identity":"8c6b223d-ce2b-415f-9ffe-e6dc191d7efe","order_by":1,"name":"Mu XU","email":"","orcid":"","institution":"Fujian Medical 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3","display":"","copyAsset":false,"role":"figure","size":410803,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9250716/v1/45ac236581eb1b16159d45f4.png"},{"id":108760099,"identity":"0770c0de-f285-4e2c-bb02-fffd0085dc27","added_by":"auto","created_at":"2026-05-08 06:26:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4294701,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9250716/v1/2bcfbff3-59dd-42ee-8522-813fd6846580.pdf"},{"id":106601022,"identity":"08e0d2b2-b816-4f07-a1f4-fa35a7931e73","added_by":"auto","created_at":"2026-04-10 10:18:05","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":1621355,"visible":true,"origin":"","legend":"","description":"","filename":"supplementBMC.docx","url":"https://assets-eu.researchsquare.com/files/rs-9250716/v1/5f755f88fc89c876365aa1a6.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research waste in randomized controlled trials of endometrial cancer: a 20-year cross-sectional study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eEndometrial cancer is a common malignancy among women experiencing peri- and postmenopause \u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e, with a global age-standardized incidence rate of 8.7 per 100,000, which is increasing annually \u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e][\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. In terms of visits, diagnosis, treatment, and follow-up monitoring, endometrial cancer imposes a significant economic burden \u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e][\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/sup\u003e. To improve the prognosis of endometrial cancer, an increasing number of randomized clinical trials (RCTs) are exploring new and potentially more effective treatments. Over the past 20 years, these RCTs have provided a high level of medical evidence to support the clinical diagnosis and treatment of endometrial cancer, which has significantly improved the quality of life of affected patients. Despite the high level of evidence provided by these RCTs and their significant contribution to advancing treatment, research waste has inevitably become a major challenge in evidence-based medicine. This implies that wasteful RCTs not only consume resources but also expose participants to unnecessary risks\u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. This waste can be generated at any stage of the study cycle. Research waste may ultimately preclude clinical practice guidelines in relevant fields from adopting results. To our knowledge, no previous study has systematically evaluated research waste in randomized controlled trials of endometrial cancer. Therefore, this study aimed to analyze the characteristics of RCTs conducted over the past 20 years and to quantify the extent of research waste, including non-publication, inadequate reporting, and avoidable design flaws. In addition, we examined whether published RCTs were cited in clinical guidelines and whether prospective data were reused, in order to identify potential targets for improving research quality and reducing waste. This cross-sectional study has been reported in line with the STROCSS guidelines \u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e.This study provides the first comprehensive assessment of research waste in endometrial cancer RCTs.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis study was based on a retrospective analysis of publicly registered randomized controlled trials (RCTs) and did not involve patient recruitment and the collection of sensitive information, so the routine ethical approval process and the signing of informed consent forms could be waived. This study was conducted in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDesign and Data Sources\u003c/h2\u003e \u003cp\u003eThe data for this study originated from ClinicalTrials.gov, the authoritative clinical trial registry \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. Specifically, we used the keywords \"Cancer OR Endometrial neoplasm OR Endometrial Carcinoma OR Endometrial Adenocarcinomas\" on the specified date (i.e., April 2025). A comprehensive search of the ClinicalTrials.gov database was conducted. The inclusion criteria were set as randomized controlled trials (RCTs) registered in the database from January 1, 2004, to January 1, 2024, with a careful review of the trial titles and abstracts to determine whether they met the study conditions. Phase I and II trials were excluded as their primary objective was to preliminarily validate efficacy and safety, and they might not follow the regular publication process. In addition, non-randomized trials, programs not associated with endometrial cancer, duplicate studies, and those whose RCTs were completed later than January 1, 2024, and were not published were excluded. This time frame was chosen to ensure that research teams had ample time to complete the paper writing, submission, peer review, and editing processes, thereby improving the quality and completeness of published results \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. Independent researchers conducted the assessment and resolved the differences through consensus.\u003c/p\u003e \u003cp\u003eIn this study, all included RCTs were ranked in descending order according to sample size, and the top 25% were defined as large-sample RCTs. Considering that North America and Europe have a high incidence of endometrial cancer and a substantial disease burden \u003csup\u003e[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e, we further classified RCTs based on the Healthcare Access and Quality (HAQ) Index of the country where the principal investigator was located\u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Trials were categorized into regions with a HAQ index\u0026thinsp;\u0026ge;\u0026thinsp;90 and \u0026lt;\u0026thinsp;90. In addition, countries were classified according to the World Bank income classification\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e, and comparisons were made between high-income and non-high-income settings.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatus of Publication\u003c/h3\u003e\n\u003cp\u003ePublication status was determined by searching PubMed and Scopus using the ClinicalTrials.gov identifier (NCT number), the principal investigator (PI) name, and relevant keywords. If no corresponding publication was identified, the PI was contacted for confirmation. If no response was received, the trial was considered unpublished \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e. If a full-text manuscript (either in print or online) is found in a peer-reviewed journal, the trial is considered to have been published. The last search was conducted on August 30, 2025.\u003c/p\u003e\n\u003ch3\u003eReporting Adequacy Assessment\u003c/h3\u003e\n\u003cp\u003eTo evaluate the Reporting completeness of each article, we conducted a detailed assessment of each report in accordance with the Consolidated Standards of Reporting Trials (CONSORT) guidelines. Considering the different characteristics of drug intervention and non-drug intervention, checklists containing 37 and 40 checkpoints were designed, respectively. One member of the research team is responsible for collecting all relevant publications and their supplementary materials. By using the PDF to Word feature of Adobe Acrobat Pro PDF software (Adobe Systems Inc.), he removed the author and journal identifiers in the document and adjusted the text layout to minimize potential biases. Subsequently, the printed documents were thoroughly reviewed and scored for each literature by two independent researchers in accordance with the CONSORT 2010 checklist. After the evaluation of every three articles is completed, the two will discuss any inconsistent opinions until a consensus is reached \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. If an article on a randomized controlled trial (RCT) involving drug intervention reports at least 27 items, or if an RCT article involving non-drug intervention covers at least 30 items, we consider its reporting to have met the adequate standard. This threshold is determined based on the median meta-analysis of reporting compliance and is also a pre-planned assessment strategy \u003csup\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e\n\u003ch3\u003eDesign Flaw Assessment\u003c/h3\u003e\n\u003cp\u003eTwo independent researchers used Cochrane tools \u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e to review the masked manuscripts and assess the risks of selection bias, performance bias, detection bias, attrition bias, reporting bias, and other biases. The risk of bias for each project is classified as low risk, unclear, or high risk. After reviewing every three manuscripts, the researchers will discuss the existing differences and reach a consensus. In statistical analysis, projects with unclear risk of bias are regarded as high-risk. This is because a vague description of the key methods will affect the judgment of the RCT's ability to provide information. In addition, the researchers also evaluated whether there were relevant systematic reviews or whether it was necessary to conduct systematic reviews in the new environment. Only when the review is cited throughout the entire manuscript, and it is believed that the review can demonstrate the necessity of conducting an RCT, is it regarded as having a systematic review. If one of the aforementioned biases occurs in the article or if the relevant systematic review is not cited, it is regarded as having avoidable design flaws.\u003c/p\u003e\n\u003ch3\u003eWhether Referenced in Guidelines and Reuse of Prospective Data\u003c/h3\u003e\n\u003cp\u003eExcluding RCTs published after January 1, 2024, for the remaining publicly released randomized controlled trials (RCTs), our initial step is to track all research literature that cites this RCT in the academic search engine Google Scholar \u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Next, two unrelated researchers personally reviewed this literature, and their task was to identify whether there were treatment or practice guidelines among them. In addition, we also evaluated whether these follow-up studies utilized the prospective data from the original RCTs for post hoc analysis, that is, whether the original RCT data were reanalyzed to obtain results other than the pre-defined primary and secondary study endpoints \u003csup\u003e[\u003cspan additionalcitationids=\"CR16\" citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eOutcome\u003c/h2\u003e \u003cp\u003eThe primary outcome of the study is to analyze the characteristics of clinical research trials conducted over the past two decades and to deeply explore the so-called \"research waste\" phenomenon, which includes unpublished research, insufficient reporting information, and avoidable design flaws. In addition, this study also explored whether published RCTs were cited by guidelines and whether prospective data were reused, as published RCTs are a prerequisite for evaluating the adequacy of reporting and design flaws. All RCTs completed after January 1, 2024, and not published were excluded from the analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eUse χ2 or Fisher's test (if the sample size is less than 5) to compare the differences in categorical variables between groups \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Simple and multivariate logistic regression models are used to identify independent risk factors associated with research waste. Variables with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the simple analysis were subsequently included in the multivariate analysis. All Statistical analyses were conducted using SPSS Statistical software 18.0 for Windows (IBM) and R Statistical software 4.0.2 (R Project for Statistical Computing). A P value less than 0.05 was considered statistically significant, and all tests were two-sided tests. The data analysis was conducted in July 2025.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eRCT development on a global scale\u003c/h2\u003e \u003cp\u003eA literature search for relevant studies, published between 2004 and 2024, retrieved 263 RCTs that fulfilled the inclusion criteria. After excluding 94 non-RCTs, 1 pediatric science trial, 21 trials not related to endometrial cancer, and 3 duplicate trials, 144 RCTs were included in the present analysis, among which 23.6% addressed drugs (n\u0026thinsp;=\u0026thinsp;34)(Fig.\u0026nbsp;1).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn terms of regional distribution, 79.8% of the RCTs had their primary researchers situated in North America or Europe, with 64 trials in North America (44.4%) and 51 in Europe (35.4%). Single-center RCTs outnumbered multi-center trials (n\u0026thinsp;=\u0026thinsp;84 [58.3%] versus [vs.] n\u0026thinsp;=\u0026thinsp;55 [38.1%], respectively). RCTs with sample sizes\u0026thinsp;\u0026gt;\u0026thinsp;350 were defined as \u0026ldquo;large\u0026rdquo;. Among the included studies, 140 (97.2%) were internally funded. Other relevant information is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of included randomized controlled trials\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRCTs,NO.(%)N\u0026thinsp;=\u0026thinsp;144\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of registration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u0026thinsp;~\u0026thinsp;2008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11(7.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2009\u0026thinsp;~\u0026thinsp;2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23(16.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2014\u0026thinsp;~\u0026thinsp;2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35(24.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2019\u0026thinsp;~\u0026thinsp;2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75(52.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntervention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e34(23.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcedure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38(26.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14(9.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58(40.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary Purpose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e69(47.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16(11.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSupport care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38(26.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e21(14.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntervention model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParallel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e132(91.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon- Parallel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12(8.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e124(86.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17(11.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3(2.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlinding\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone or open label\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e87(60.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26(18.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble and more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31(21.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMISS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1(0.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecruitment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonocentric\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e84(58.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulticenter\u003c/p\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e55(38.1)\u003c/p\u003e \u003cp\u003e5(3.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFunder Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone or departmental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140(97.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry or other external\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4(2.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion of PI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64(44.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51(35.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eother\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23(16.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiss\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6(4.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion of PI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAQ\u0026lt;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e122(84.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHAQ\u0026thinsp;\u0026ge;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16(11.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMISS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6(4.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion of PI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e121(84.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-high income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17(11.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMISS 6(4.2)\u003c/h2\u003e \u003cp\u003eThe number of RCTs addressing endometrial cancer has been increasing annually, reaching a peak between 2019 and 2024, with a total of 75 trials (52.1%) compared with only 11 (7.6%) from 2004 to 2008. From 2003 to 2024, the number of published RCTs across different categories has demonstrated significant fluctuations (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). The number of RCTs for other and surgical intervention types occupied a larger share, and there were more RCTs addressing surgical and drug intervention than those for equipment types, with the number of RCTs in the equipment intervention category the lowest (Figure S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eOverview of research waste\u003c/h2\u003e \u003cp\u003eAmong the 144 included randomized controlled trials (RCTs), only 57 (39.6%) were published. Among published trials, 30 (52.6%) achieved adequate reporting according to the CONSORT checklist, while 25 (43.9%) exhibited avoidable design flaws. Overall, 129 trials (89.6%) had at least one feature of research waste. A summary of these findings is presented in Fig.\u0026nbsp;2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eNon-publication\u003c/h2\u003e \u003cp\u003eOverall, 57 registered RCTs (39.6%) were published in peer-reviewed journals, and their full texts were available for review, 87 (60.4%) had not yet been published. Compared with published RCTs, the unpublished RCTs may be related to the procedure (21 trials [38.89%] vs. 16trials [21.92%]; P\u0026thinsp;=\u0026thinsp;0.017), and are more likely to come from single-center recruitment (31trials [57.41%] vs. 16trials [21.92%]; P\u0026thinsp;=\u0026thinsp;0.034), and have a smaller sample size (58trials [90.63%] vs. 53trials [72.6%]; P\u0026thinsp;=\u0026thinsp;0.009). Additionally, published RCTs were more likely to be from high-income countries and those with a high sociodemographic index (66trials [90.41%] vs 45trials [83.3%]; P\u0026thinsp;=\u0026thinsp;0.031) (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In multivariate analysis, RCTs addressing drug intervention for endometrial cancer were more likely to be published (odds ratio [OR] 0.330 [95% confidence interval (CI) 0.136 0.801]; P\u0026thinsp;=\u0026thinsp;0.014) (Table S2).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eReporting Adequacy\u003c/h2\u003e \u003cp\u003escores for the 57 published RCTs were evaluated according to the Consolidated Standards of Reporting Trials (CONSORT) checklist. Thirty (52.6%) RCTs were determined to have adequate reporting. These well-reported RCTs were more likely to adopt double- or even multi-blind designs (11 [36.7%] vs. 2 [7.4%]; P\u0026thinsp;=\u0026thinsp;0.025) (Table S3). The most significant deficiencies in 20 reports addressing drug intervention were the lack of information regarding the availability of the trial protocol (present in 10% of the RCTs) and the description of the randomization method (35.0%). The most common deficiencies in 37 reports unrelated to drug intervention were the reporting of information regarding the availability of the trial protocol (present in 35.1% of RCTs), details of blinding (32.4%), and details for evaluating or strengthening the compliance of providers with the protocol (51.3%) (Table S4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eDesign limitations\u003c/h2\u003e \u003cp\u003eOf the 57 published RCTs, 14 (24.6%) did not cite systematic reviews in the main text and 25 (43.9%) had\u0026thinsp;\u0026ge;\u0026thinsp;1 feature(s) indicating a high or unclear risk of bias. The most common factors associated with the risk of bias were deviations from established interventions (15 trials [26.3%]) and outcome measures (13 trials [22.8%]) (Fig.\u0026nbsp;3). Considering these factors, 25 (43.9%) RCTs were determined to have avoidable design flaws. Compared with these trials, RCTs with unavoidable design flaws were more likely to have double- or even multi-blind designs (11 [34.4%] vs. 2 [8.0%]; P\u0026thinsp;=\u0026thinsp;0.032) (Table S5).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eResearch Waste\u003c/h2\u003e \u003cp\u003eConsidering the combination of publication status, adequate reporting, and avoidable design flaws, 129 (89.6%) of the 144 RCTs had\u0026thinsp;\u0026ge;\u0026thinsp;1 characteristic(s) of research waste. These RCTs were more likely to have no or open-label blinding than the 15 no-research waste RCTs (82 [63.6%] vs 5 [33.3%]; P\u0026thinsp;=\u0026thinsp;0.002). In addition, RCTs with no research waste were more likely to be drug related (8 [53.3%] vs. 26 [20.2%]; P\u0026thinsp;=\u0026thinsp;0.030) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of trial characteristics according to the presence of research waste\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eWithout research waste, No. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003eWith research waste, No. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003eTotal, No. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunder Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone or departmental\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e15(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e125(96.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e140(97.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndustry or other external\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e4(3.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e4(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntervention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcedure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4(26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e34(26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e38(26.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.030*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrug\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e8(53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e26(20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e34(23.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDevice\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e13(10.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e14(9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e56(43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e58(40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecruitment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle centre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e9(60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e75(58.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e84(58.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.739\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulticenter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e6(40.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e49(38.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e55(38.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e5(3.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e5(3.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e12(92.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e112(85.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e124(86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1(7.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e16(12.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e17(11.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e3(2.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e3(2.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntervention modle\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParallel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e15(100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e117(90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e132(91.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.459\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Parallel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0(0.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e12(9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e12(8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlinding\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone or open label\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e5(33.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e82(63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e87(60.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e25(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e26(18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDouble or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e9(60.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e22(17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e31(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary purpose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e8(53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e61(47.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e69(47.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.657\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e7(46.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e68(52.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e75(52.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo. of participants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e12(80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e105(81.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e117(81.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e24(18.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e27(18.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePI region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e14(93.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e114(88.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e128(88.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e1(6.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e15(11.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e16(11.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePI region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e8(53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e56(43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e64(44.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEurope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e46(35.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e49(34.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e4(26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e27(20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e31(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePI region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-high SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e25(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e28(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e12(80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e104(80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e116(80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePI region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e12(80.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e104(80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e116(80.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-high income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e3(20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e25(19.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e28(19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTime of registration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2004\u0026thinsp;~\u0026thinsp;2013.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2(13.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e29(22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e31(21.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.628\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013.06\u0026thinsp;~\u0026thinsp;2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e13(86.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e \u003cp\u003e100(77.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e113(78.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn further analyses, single-blind and multi-blind designs were associated with a lower odds of study waste (OR, 0.291 [95% CI 0.094\u0026ndash;0.9060]; P\u0026thinsp;=\u0026thinsp;0.033) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariable logistic regression analysis of factors associated with research waste\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnivariate analysis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMultivariate analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOR(95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime of registration\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2003\u0026thinsp;~\u0026thinsp;2013.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2013.06\u0026thinsp;~\u0026thinsp;2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.531(0.113\u0026thinsp;~\u0026thinsp;2.487)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.421\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePrimary purpose\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTreatment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.274(0.436\u0026thinsp;~\u0026thinsp;3.720)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.658\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eIntervention\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-pharmacological-related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePharmacological-related\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.221(0.073\u0026thinsp;~\u0026thinsp;0.665)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.007**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.767(0.220\u0026thinsp;~\u0026thinsp;2.673)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eArm\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.714(0.209\u0026thinsp;~\u0026thinsp;14.086)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBlinding\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNone or open label\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.287(0.092\u0026thinsp;~\u0026thinsp;0.889)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.291(0.094\u0026thinsp;~\u0026thinsp;0.906)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.033*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRecruitment\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle centre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMulticenter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.980(0.328\u0026thinsp;~\u0026thinsp;2.926)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.971\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNo. of participants\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.914(0.239\u0026thinsp;~\u0026thinsp;3.494)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePI region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-high SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh SDI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.040(0.273\u0026thinsp;~\u0026thinsp;3.965)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePI region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-high income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.040(0.273\u0026thinsp;~\u0026thinsp;3.965)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePI region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.842(0.226\u0026thinsp;~\u0026thinsp;15.027)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.568\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePI region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorth America\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.490(0.510\u0026thinsp;~\u0026thinsp;4.354)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e* p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eReferenced in Guidelines and the Reuse of Prospective Data\u003c/h2\u003e \u003cp\u003eTwo RCTs published within 1 year of 2023 were excluded, with 15 trials (26.3%) cited in the corresponding guidelines. In particular, multicenter trials (12 [80.0%] vs. 14 [33.3%]; P\u0026thinsp;=\u0026thinsp;0.01) were more likely to be sourced from North American countries (11 [73.3%] vs. 20 [47.6%]; P\u0026thinsp;=\u0026thinsp;0.025) and to use treatment as the primary objective of the trial (n\u0026thinsp;=\u0026thinsp;12 [80.0%] vs. n\u0026thinsp;=\u0026thinsp;3 [20.0%]; P\u0026thinsp;=\u0026thinsp;0.025) (Table S6). In further analyses, multicenter RCTs were more likely to be cited in guidelines (OR 0.158 [95% CI] 0.037\u0026ndash;0.679; P\u0026thinsp;=\u0026thinsp;0.013) (Table S7). Finally, prospective data from only 1 RCT (1.8%) were reused, although there were no significant differences between the groups (Table S8).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eFor the first time, this cross-sectional study analyzed the characteristics of 144 RCTs investigating endometrial cancer over the past 20 years and found a high level of research waste, with 89.6% of the RCTs having\u0026thinsp;\u0026ge;\u0026thinsp;1 features of study waste. Fifty-seven (39.6%) RCTs were published and were available for full-text review, of which 25 (17.3%) were judged to have avoidable design flaws. Thirty RCTs (20.8%) were adequately reported. In addition, RCTs published in the most recent year (i.e.,2024) were excluded and 15 trials (10.4%) were cited in the corresponding guidelines. Prospective data from 1 RCT (0.7%) were reused. In further analyses, single- and multi-blind designs were associated with a lower odds of study waste.\u003c/p\u003e \u003cp\u003eThis study found a significant upward trend in the number of patients with endometrial cancer and RCTs over the past 2 decades. The number of RCTs conducted between 2019 and 2024 (n\u0026thinsp;=\u0026thinsp;75 [52.1%]) increased significantly compared with that from 2004 to 2008 (n\u0026thinsp;=\u0026thinsp;11 [7.6%]), which is consistent with the continued upward trend in the incidence of endometrial cancer worldwide. With the increasing disease burden, the need to explore new treatment methods has become increasingly urgent. The increase in the number of RCTs reflects the positive response of the scientific research community to improving the prognosis of affected patients and provides potential evidence supporting the optimization of clinical diagnosis and treatment strategies.\u003c/p\u003e \u003cp\u003eIn terms of regional distribution, the principal investigators of 79.8% of the RCTs were situated in North America and Europe. This distribution is closely associated with the allocation of medical resources, the intensity of scientific research investment, and the Healthcare Access and Quality (HAQ) index. Regions with high HAQ usually have a better scientific research infrastructure, sufficient financial support, and a mature collaboration network, thus making it easier to conduct RCTs.\u003c/p\u003e \u003cp\u003eAs a common gynecological malignancy, the diagnosis and treatment of endometrial cancer requires multidisciplinary collaboration, including surgery, drugs, and other interventions, which is highly consistent with the characteristics of surgical disease diagnosis and treatment. Both rely on RCTs to verify the effectiveness of interventions; however, both encounter the serious problem of research waste. Our analysis of 144 endometrial cancer RCTs worldwide over the past 20 years revealed that 89.6% had\u0026thinsp;\u0026ge;\u0026thinsp;1 feature(s) of study waste (e.g. unpublished, inadequate reporting, or avoidable design flaws), and their core deficiencies highly overlapped with the common problems of surgical RCTs. These issues limit the effective translation of evidence into clinical practice and hinder progress in endometrial cancer research.\u003c/p\u003e \u003cp\u003eThere is a significant imbalance between the waste of drugs and surgical interventions in research investigating endometrial cancer. Among the 144 RCTs addressing endometrial cancer, only 23.6% (n\u0026thinsp;=\u0026thinsp;34) were drug intervention studies, while nearly 80% were surgery and other non-drug intervention studies. However, multivariate analysis confirmed that the publication probability of a drug-intervention RCT was significantly higher, and 53.3% (8/15) of the RCTs without research waste were related to drugs, which was much higher than the proportion of drug interventions in the overall number of RCTs (20.2% [26/129]) (P\u0026thinsp;=\u0026thinsp;0.030). Among the unpublished endometrial cancer RCTs, 38.89% (21/54) were related to procedural interventions such as surgery, which was significantly higher than the 21.92% (16/73) of the published RCTs (P\u0026thinsp;=\u0026thinsp;0.017). The unpublished RCTs preferred a single-center design (57.41% vs. 21.92%; P\u0026thinsp;=\u0026thinsp;0.034) and smaller sample size (90.63% vs. 72.6%; P\u0026thinsp;=\u0026thinsp;0.009).\u003c/p\u003e \u003cp\u003eThis characteristic of \u0026ldquo;easy publication of drug research and high waste of surgical research\u0026rdquo; was more prominent among surgical RCTs. In a study investigating surgical RCTs, Chapman et al \u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e reported that 85.2% of surgical RCTs exhibited research waste. Due to the high design complexity and difficulty in standardization, the unpublished rate of surgical RCTs (approximately 62%) was significantly higher than that for adjuvant drug therapy RCTs (approximately 38%). The analysis of RCTs in the field of gastric cancer also revealed that adequate reporting rate of non-drug interventions (including surgery) RCTs (52.9%) was significantly lower than that for RCTs (87.5%), and 77.8% of surgery-related RCTs had avoidable design defects \u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Surgical intervention studies, both in endometrial cancer and in surgical fields (such as those for gastric cancer and gastrointestinal surgery), are research areas exhibiting the most waste due to insufficient resource allocation and difficulties in design and implementation. This intervention imbalance is essentially a common problem of \u0026ldquo;unreasonable allocation of resources and failure to use resources where they can be used to make a difference\u0026rdquo; encountered by the 2 types of research.\u003c/p\u003e \u003cp\u003eThe vagueness of details in surgical RCTs addressing endometrial cancer coincides with common problems in surgical RCTs. According to the CONSORT guidelines, only 32.4% of the 37 non-pharmacological (including surgery) endometrial cancer RCTs clearly described the details of blinding, and 51.3% did not describe specific measures to \u0026ldquo;evaluate or enhance provider adherence to the protocol\u0026rdquo;. However, the missing rates of key details, such as surgical technical standards (e.g., incision location for laparoscopic surgery and, the extent of lymph node dissection) and intraoperative adjustment strategies, were higher. Simultaneously, 22.8% (13/57) of the published RCTs had the problem of \u0026ldquo;non-standard outcome measurement\u0026rdquo;.\u003c/p\u003e \u003cp\u003eThe physical characteristics of surgery make \u0026ldquo;complete blinding\u0026rdquo; virtually impossible, which constitutes an inherent source of bias in both surgical procedures and surgical RCTs for endometrial cancer. Among RCTs addressing endometrial cancer RCTs, 63.6% (82/129) used a non-blinded or open-label design, which was significantly higher than that for RCTs with no research waste (33.3% [5/15]) (P\u0026thinsp;=\u0026thinsp;0.002). Multivariate analysis further confirmed that a single- or multi-blind design was associated with a lower risk for research waste (OR\u0026thinsp;=\u0026thinsp;0.291 [95% CI 0.094\u0026ndash;0.906]; P\u0026thinsp;=\u0026thinsp;0.033) because patients were aware of the group assignment by the type of surgical incision (e.g., laparoscopic vs. open surgery), and surgeons were aware by the surgical method. In this study, 43.9% (25/57) of published RCTs had a \u0026ldquo;high or unclear risk of bias\u0026rdquo;.\u003c/p\u003e \u003cp\u003eIn response to these common problems, feasible strategies have been explored in the field of surgery to, provide a reference for research investigating endometrial cancer. In terms of intervention imbalance, the field of gastric cancer has been encouraged to promote multicenter collaboration and integrate single-center surgical RCTs into multicenter studies \u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e. Research in other fields has highlighted the importance of optimizing resource allocation and promoting multicenter collaboration to improve the quality of randomized trials \u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e. Similar strategies could be applied in endometrial cancer research to enhance study design and reduce research waste. Research investigating endometrial cancer can refer to this concept by establishing a multicenter collaboration platform with gynecological cancer centers, and add a \u0026ldquo;surgical RCT special\u0026rdquo; in the allocation of scientific research funds to give priority to research investigating laparoscopic, robot-assisted, and other technologies.\u003c/p\u003e \u003cp\u003eIn terms of standardizing reporting, the gastric cancer and GERD domains should advocate for reporting adequacy of non-drug intervention RCTs to be assessed using a 40-point checklist designed according to the CONSORT guidelines. In addition, special reporting standards should be developed to describe the details of surgical incision location and extent of lymph node dissection in the text or supplementary materials, and to unify the diagnostic criteria for postoperative complications (such as vaginal stump bleeding). In the process of bias control, based on the Cochrane Collaboration tool to assess the risk of bias, studies investigating endometrial cancer can also use third-party assessment of postoperative pain, quality of life, and other outcomes to reduce bias in RCTs that cannot be blinded, such as those for laparoscopic or open surgeries.\u003c/p\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eIn the analysis of research waste \u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e, first of all, quantifying research waste is also a complicated task. This waste is by no means limited to the 3 elements defined in this study. Categories such as \"duplicate studies\" and \"data not shared\" may underestimate the actual extent of waste. Second, the various endpoints were collected manually, which may be related to measurement error. However, we minimized measurement error by having two independent investigators review each RCT and resolve disagreements through discussion. Third, although ClinicalTrials.gov is a comprehensive clinical trial registry, accounting for more than 80% of all clinical studies on the WHO portal \u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e, the World Health Organization also recognizes other registries in specific countries and regions \u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e, and RCTs from these registries were not included in this study. For example, studies from other databases (such as the Chinese Clinical Trial Registry) were omitted, resulting in underrepresentation globally. Fourth, using \"the location of the principal investigator\" to represent the study area may not correspond to the actual implementation site (such as transnational cooperative research), which may lead to bias in the analysis of regional distribution.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, research waste is highly prevalent in randomized controlled trials of endometrial cancer. Significant deficiencies remain in publication, reporting, and study design. Targeted efforts to improve trial methodology, enhance reporting quality, and promote multicenter collaboration are urgently needed to reduce research waste and improve the efficiency of evidence translation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eEthical approval\u003c/h2\u003e \u003cp\u003eThis study was a retrospective analysis based on publicly registered randomized controlled trials (RCTs), which did not involve patient recruitment and collection of sensitive information, the routine ethical approval process and the signing of the informed consent form could be exempted.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConsent\u003c/h2\u003e \u003cp\u003eThis study was a retrospective analysis based on publicly registered randomized controlled trials (RCTs), which did not involve patient recruitment and collection of sensitive information, the routine ethical approval process and the signing of the informed consent form could be exempted.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of interest disclosure\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eResearch registration unique identifying number((UIN)\u003c/h2\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eAssistance with the study\u003c/h2\u003e \u003cp\u003eWe thank all contributors to this study, including nurses, pathologists, postgraduate trainees, statisticians, reviewers, and editors.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJin-Peng Yi, and Mu Xu contributed equally to this work and should be considered co-first authors.J.P.Y. and M.X. drafted the main manuscript text and prepared the figures and tables. J.P.Y. and M.X. performed the literature search, data extraction, and quality assessment (including CONSORT and Risk of Bias evaluations).Y.F.D. and P.M.S. conceptualized and designed the study. L.Y.Z. assisted in data curation and statistical analysis. Y.F.D. and P.M.S. supervised the project and critically revised the manuscript for important intellectual content.All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated or analyzed during this study are included in this published article. Further details are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCrosbie EJ, Kitson SJ, McAlpine JN, Mukhopadhyay A, Powell ME, Singh N. Endometrial cancer. 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Published 2020 Aug 3.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-9250716/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9250716/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground \u003c/strong\u003eRandomized controlled trials (RCTs) provide high-level evidence for the management of endometrial cancer. However, research waste—including non-publication, inadequate reporting, and avoidable design flaws—remains a major challenge in clinical research. To our knowledge, no previous study has systematically evaluated research waste in RCTs of endometrial cancer. This study aimed to assess the characteristics of RCTs and quantify the extent of research waste over the past 20 years.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods \u003c/strong\u003eWe conducted a cross-sectional analysis of RCTs registered on ClinicalTrials.gov between January 2004 and January 2024. Publication status was identified through PubMed and Scopus. Reporting quality was evaluated using the CONSORT checklist, and methodological quality was assessed using the Cochrane risk-of-bias tool. Research waste was defined as the presence of at least one of the following: non-publication, inadequate reporting, or avoidable design limitations. Logistic regression analyses were performed to identify factors associated with research waste.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults \u003c/strong\u003eA total of 144 RCTs were included, of which 57 (39.6%) were published. Overall, 129 trials (89.6%) exhibited at least one feature of research waste. Among published trials, 52.6% had adequate reporting, and 43.9% showed avoidable design flaws. Trials with double- or multi-blind designs were more likely to be adequately reported and had a lower risk of research waste. Multicenter trials and those conducted in North America were more likely to be cited in clinical guidelines. Prospective data reuse was rare (1.8%). In multivariate analysis, blinding design was independently associated with reduced research waste.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions \u003c/strong\u003eResearch waste is highly prevalent in RCTs of endometrial cancer. Improving trial design, promoting multicenter collaboration, and strengthening reporting standards may help reduce research waste and enhance the translation of evidence into clinical practice.\u003c/p\u003e","manuscriptTitle":"Research waste in randomized controlled trials of endometrial cancer: a 20-year cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-10 10:17:54","doi":"10.21203/rs.3.rs-9250716/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"995c2ac9-0b20-4924-b9b4-e0e29877d4bc","owner":[],"postedDate":"April 10th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-08T06:21:52+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-06T07:44:00+00:00","index":44,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-05T07:24:53+00:00","index":43,"fulltext":""},{"type":"reviewerAgreed","content":"190222680948406568240923805961711218140","date":"2026-05-05T04:25:41+00:00","index":42,"fulltext":""},{"type":"reviewerAgreed","content":"280442326861229186996031558268082554380","date":"2026-05-05T01:12:50+00:00","index":41,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T06:26:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-10 10:17:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9250716","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9250716","identity":"rs-9250716","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}