The Diagnostic Power of General Temperament Questionnaire Compared to Uterine Temperament Questionnaire in Determining Uterine Temperament of Infertile Women: A Cross-Sectional Analytical Study

The Diagnostic Power of General Temperament Questionnaire Compared to Uterine Temperament Questionnaire in Determining Uterine Temperament of Infertile Women: A Cross-Sectional Analytical 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 Article The Diagnostic Power of General Temperament Questionnaire Compared to Uterine Temperament Questionnaire in Determining Uterine Temperament of Infertile Women: A Cross-Sectional Analytical Study Fazeleh Fazlollahpour-Rokni, Seyede-Sedigheh Yousefi, Marzieh Zamaniyan, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7901926/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Background Mizaj (temperament) is a fundamental concept in Persian medicine, critically influencing the prevention, treatment, and prognosis of diseases. The uterus, as a key reproductive organ, has its own specific temperament, which is assessed by a specialized questionnaire. Additionally, an individual’s general temperament may also reflect uterine temperament due to the holistic approach of Persian medicine. Objectives This study aimed to evaluate the diagnostic accuracy of the General Temperament Questionnaire (GTQ) versus the Uterine Temperament Questionnaire (UTQ) for determining uterine temperament in infertile women. Methods This cross-sectional analytical study included 62 infertile women. Participants completed both the GTQ and UTQ. Diagnostic performance was assessed using sensitivity, specificity, and Cohen’s kappa (κ). Associations with clinical variables were analyzed. Results The UTQ identified cold-wet (50.0%) as the predominant uterine temperament, followed by cold-dry (40.32%). Strong agreement was found between the UTQ and GTQ (κ = 0.72, p < 0.001). Both questionnaires showed significant association with infertility etiology (p = 0.001), linking cold-wet temperament to Polycystic Ovarian Syndrome (PCOS) and cold-dry to primary ovarian insufficiency (POI). BMI was significantly higher in wet temperament categories (UTQ p = 0.016, GTQ p = 0.004). Conclusion Although general and uterine temperament assessments show substantial agreement, the UTQ demonstrates stronger alignment with clinical biomarkers and provides more precise organ-specific diagnosis. Using the UTQ is recommended for targeted diagnosis of uterine dystemperament in infertile women to facilitate personalized treatment strategies and potentially prevent ineffective treatments in PM. Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Infertility Female Uterine Temperament Persian Medicine Diagnostic Accuracy Questionnaire Introduction Infertility is defined as the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse, or after six months for women over the age of 40( 1 , 2 ). According to recent World Health Organization (WHO) reports, approximately 17.5% of the adult population—affecting roughly one in six people worldwide—experiences infertility( 3 , 4 ). Contributing factors include lifestyle changes, tobacco use, alcohol consumption, and obesity( 5 , 6 ). In females, infertility most commonly results from ovulatory disorders, tubal and pelvic pathologies, and uterine or cervical abnormalities( 7 ). Treatment strategies for infertility vary based on the underlying etiology ( 8 ). For women with ovulation disorders, conventional treatments include ovulation induction using medications such as clomiphene citrate, injectable gonadotropins, and aromatase inhibitors like letrozole ( 9 , 10 ). However, these pharmacological interventions are associated with risks such as multiple pregnancies, ovarian hyperstimulation syndrome (OHSS), and a potential increase in the risk of ovarian and breast malignancies ( 11 ). Assisted reproductive technologies (ART), particularly in vitro fertilization (IVF), represent another treatment avenue, though success rates remain limited with approximately two-thirds of cycles unsuccessful( 12 , 13 ). In recent decades, growing interest has emerged in complementary and alternative medicine approaches for managing infertility ( 14 , 15 ). Persian Medicine (PM), with its historical foundation and holistic principles, serves as a complementary system that can be integrated with modern medicine to address gaps in disease prevention, health promotion, and treatment( 16 ). Central to PM is the concept of mizaj (temperament), which plays a determining role in prevention, treatment, and prognosis of diseases. Temperament influences emotional and physical characteristics as well as physiological functions( 17 ). Temperaments are classified into four singular types (hot, cold, dry, wet), four compound types (hot-wet, hot-dry, cold-wet, cold-dry), and a moderate type( 18 , 19 ). According to PM theory, health reflects a state of temperamental balance, whereas dystemperament—a deviation from the optimal temperament of an organ or the entire body—can lead to functional impairment and disease ( 17 ). Alterations in uterine temperament are among the most significant disorders related to the uterus in PM, affecting not only reproductive function but also the physiological activities of related organs ( 20 , 21 ). PM texts associate numerous gynecological conditions—including infertility, recurrent miscarriage, oligomenorrhea, amenorrhea, menorrhagia, vaginitis, cervicitis, urinary incontinence, and pelvic pain—with uterine dystemperament ( 22 ). Accurate diagnosis and correction of uterine dystemperament may improve clinical outcomes, reduce the risk of treatment failure and recurrence, and decrease associated healthcare costs ( 23 ). Uterine temperament is assessed using a specialized questionnaire and diagnostic protocol based on clinical signs and uterine characteristics ( 17 , 22 ). PM literature describes the uterus as a vital organ whose dystemperament can influence overall health, potentially leading to systemic imbalance ( 24 – 26 ). Although a general temperament questionnaire has been developed and validated for assessing whole-body temperament in PM( 27 , 28 ), and several studies have examined both general and uterine temperaments in gynecological disorders, no study has specifically evaluated the diagnostic accuracy of the general temperament questionnaire for determining uterine temperament in infertile women, or assessed the level of agreement between these two instruments ( 20 , 21 , 23 ). In PM, the general temperament questionnaire is commonly used for diagnosing various diseases( 19 ). Given the holistic of PM, practitioners can often infer the temperament of specific organs from the overall body temperament( 18 ). Therefore, if the general temperament questionnaire demonstrates adequate diagnostic accuracy in assessing uterine temperament compared to the dedicated uterine temperament questionnaire, it could streamline the diagnostic and therapeutic processes for uterine conditions and serve as a viable alternative to the uterine temperament questionnaire. Given the clinical relevance of uterine temperament diagnosis—particularly in the context of infertility management—and the comparative practicality of evaluating general versus uterine temperament, this study aimed to evaluate and compare the diagnostic accuracy of the General Temperament Questionnaire (GTQ) with the specialized Uterine Temperament Questionnaire (UTQ) for determining uterine temperament in infertile women. An accurate diagnosis of uterine temperament may facilitate early and preventive interventions, potentially reducing the risk of progression to irreversible infertility or disease recurrence. The findings may provide valuable insights for patients, PM specialists, gynecologists, and healthcare policymakers. Methods Study Design and Setting This cross-sectional analytical study was conducted at the Infertility Clinic of Imam Khomeini Hospital, a major referral center in Sari, Iran, from June 2025 to September 2025. Ethical Considerations The study protocol was reviewed and approved by the Ethics Committee of Mazandaran University of Medical Sciences (Ethical code: IR.MAZUMS.REC.1404.120). Written informed consent was obtained from all individual participants. The study procedures adhered to the ethical standards of the institutional research committee and the 1964 Helsinki Declaration and its subsequent amendments. Participants A total of 62 infertile women were enrolled using a convenience sampling method. The inclusion criteria were: age between 18–40 years, diagnosis of primary or secondary infertility due to ovarian causes or unexplained factors, and provision of informed consent. Exclusion criteria included: infertility due to anatomical factors (e.g., uterine fibroids, hydrosalpinx) or male factors, current tobacco use or alcohol consumption, history of any chronic underlying diseases, and use of hormonal medications that could influence temperament assessment. Sample Size The sample size was calculated as 58 participants using the formula for comparing a mean with a standard value, based on data from a previous study by Tansaz et al( 17 ). With a mean general temperament score of 138.88 ± 17.61 (σ), a significance level (α) of 0.05, a power (1-β) of 80%, and a margin of error (d) of 6.5, the calculation was as follows: n=(z1 − α/2 + z1 − β)2 × σ2d2=(1.96 + 0.84)2×(17.61)2(6.5)2 = 58n = d2(z1 − α/2​+z1 − β​)2 × σ2​=(6.5)2(1.96 + 0.84)2×(17.61)2​=58 The minimum sample size was determined to be 58 participants. To enhance data reliability and account for potential missing information, data were collected from 62 participants. Instruments and Data Collection Data were collected using three tools: A Demographic and Clinical Information Questionnaire to record age, weight, height, body mass index, education, occupation, and cause of infertility. The General Temperament Questionnaire (GTQ) : This validated 20-item instrument developed by Salmannejad et al( 27 ). assesses overall body temperament (Cronbach's α = 0.74). Its sensitivity (63–80%) and specificity (57–85%) have been established. Temperament is classified as Cold (score ≤ 46), Moderate (47–49), or Warm (≥ 50) for the first 15 items, and Wet (≤ 14), Moderate ( 15 – 16 ), or Dry (≥ 17) for the remaining 5 items( 27 ). The Uterine Temperament Questionnaire (UTQ) : This validated 12-item instrument developed by Tansaz et al( 17 ). specifically evaluates uterine temperament (Cronbach's α > 0.70; content validity confirmed by experts). Scores for warmth/coldness (9 items) range from 9 (cold) to 63 (warm), with 36 as moderate. Scores for wetness/dryness (3 items) range from 3 (dry) to 21 (wet). Raw scores were normalized to a 1–7 scale by dividing by the number of items in each subsection for final analysis( 17 ). Procedure Eligible participants were recruited from the infertility clinic using convenience sampling based on inclusion and exclusion criteria. Under the supervision of a Persian medicine specialist, participants completed the demographic questionnaire, GTQ, and UTQ during their clinic visit. All data were collected in a private clinical setting to ensure confidentiality and consistency. Statistical Analysis Descriptive statistics were presented using mean, standard deviation mean ± standard deviation (SD), percentage (%), and frequency. The normality assumption was assessed using the Kolmogorov-Smirnov test. Agreement between questionnaires was assessed using Cohen's kappa coefficient (κ). Associations between temperament types and clinical variables were examined using appropriate statistical tests (chi-square for categorical variables, ANOVA for continuous variables based on distribution). Data analysis was performed using SPSS version 22. A p-value < 0.05 was considered statistically significant for all analyses. Results Participant Characteristics A total of 62 infertile women with a mean age of 32.9 ± 4.5 years participated in the study. The majority of participants were housewives (67.7%), had a medium income level (52.5%), held a university education (63.9%), and were diagnosed with primary infertility (72.1%). The most common etiology of infertility was Polycystic Ovary Syndrome (PCOS), accounting for 52.6% of cases. The mean duration of marriage and infertility were 8.3 ± 3.9 and 5.2 ± 3.3 years, respectively. The mean Body Mass Index (BMI) was 27.97 ± 5.27 Who are categorized as overweight.. Detailed demographic and clinical characteristics of the participants are presented in Table 1 . Table 1 Demographic and Clinical Characteristics of Study Participants (n = 62) Variable Frequency (N) Percentage (%) Occupation Housewife 42 67.7 Employee 9 14.5 Self-employed 11 17.7 Income Level Low 5 8.2 Medium 32 52.5 High 24 39.3 Education Below diploma 10 16.4 Diploma 12 19.7 University 39 63.9 Infertility Type Primary 44 72.1 Secondary 17 27.9 Etiology PCOS 30 52.6 POI 17 29.8 Endometriosis 8 14.0 Unexplained 2 3.5 Variable Mean Sd Age (years) 32.9 4.5 Marriage_Duration (years) 8.3 3.9 Infertility_Duration (years) 5.2 3.3 BMI (kg/m²) 27.974 5.273 Agreement between the General Temperament Questionnaire (GTQ) and Uterine Temperament Questionnaire (UTQ) The frequency distribution of temperament types according to both questionnaires is shown in Table 2 . Based on the UTQ, the most prevalent uterine temperament was cold-wet (50.0%), followed by cold-dry (40.32%), hot-dry (6.45%), and hot-wet (3.23%). According to the GTQ, the most prevalent general temperament was cold-wet (53.23%), followed by cold-dry (37.09%), hot-wet (6.45%), and hot-dry (3.23%). The agreement between the two questionnaires, as assessed by Cohen’s Kappa coefficient, was 0.72 (p < 0.001. A kappa coefficient above 0.6 indicates a good level of agreement( 29 ), and the obtained kappa of 0.72 signifies a strong and appropriate concordance between the two questionnaires. Therefore, the general body temperament questionnaire demonstrates adequate diagnostic accuracy compared to the uterine temperament questionnaire in diagnosing uterine temperament. Table 2 Cross-tabulation of Temperament Types Between Two Questionnaires Temparement General Uterine - Cold-Dry Hot-Dry Cold-Wet Hot-Wet total (%) Cold-Dry 22 0 3 0 25 (40.32) Hot-Dry 0 1 1 2 4 (6.45) Cold-Wet 1 1 28 1 31 (50.0) Hot-Wet 0 0 1 1 2 (3.23) total (%) 23 (37.09) 2 (3.23) 33 (53.23) 4 (6.45) 62 (100.0) *Cohen’s κ = 0.72 (p < 0.001)* Association of Temperament with Qualitative Variables The associations between temperament types (from both questionnaires) and qualitative variables are presented in Table 3 . Chi-square analysis revealed that the distribution of temperament types was not significantly associated with job status, income level, education, or type of infertility (primary vs. secondary) for either questionnaire (p > 0.05 for all). However, a highly significant association was found between temperament and the etiology of infertility for both the UTQ and the GTQ (p = 0.001 for both). Table 3 Association Between Qualitative Variables and Temperament Types Variable Uterine Temperament General Temperament cold- dry hot-dry cold- wet hot- wet cold- dry hot-dry cold- wet hot- wet Occupation Housewife 17 2 21 2 16 1 23 2 Employee 4 0 5 0 3 1 5 0 Self-employed 4 2 5 0 4 0 5 2 p-value 0.643 0.482 Income Level Low 4 0 1 0 3 0 2 0 Medium 10 2 20 0 9 2 19 2 High 11 2 9 2 11 0 11 2 p-value 0.176 0.575 Education Below diploma 3 1 6 0 3 0 6 1 diploma 5 0 7 0 4 0 8 0 University 17 3 17 2 16 2 18 3 p-value 0.780 0.761 Infertility Type Primary 18 2 24 0 18 2 23 1 Secondary 6 2 7 2 4 0 10 3 p-value 0.083 0.097 Etiology PCOS 3 3 23 1 3 2 23 2 POI 13 0 4 0 12 0 5 0 Endometriosis 5 0 3 0 4 0 4 0 Unexplained 1 1 0 0 1 0 0 1 p-value 0.001 0.001 Association of Temperament with Quantitative Variables The relationship between quantitative variables and the different temperament categories is detailed in Table 4 . One-way ANOVA tests showed that variables such as age, marriage duration, and infertility duration were not significantly different across the various temperament categories for either questionnaire (p > 0.05). In contrast, BMI showed a statistically significant association with temperament types in both the UTQ (p = 0.016) and the GTQ (p = 0.004). Post-hoc analyses indicated that in both classifications, mean BMI was higher in cold-wet and hot-wet temperaments compared to cold-dry and hot-dry categories. Table 4 Association Between Quantitative Variables and Temperament Types Variable Uterine Temperament General Temperament cold- dry hot-dry cold- wet hot- wet p-value cold- dry hot-dry cold- wet hot- wet p-value Age (years) 33.5 34.0 32.5 30.5 0.668 33.3 32.5 32.3 35.5 0.558 Marriage Duration (years) 8.5 7.8 8.3 7.0 0.946 7.9 7.0 8.7 7.3 0.779 Infertility Duration (years) 5.0 2.5 5.9 2.5 0.141 5.1 5.5 5.6 3.0 0.525 BMI (kg/m²) 25.5 27.4 29.9 28.1 0.016 25.1 24.8 29.9 29.9 0.004 Discussion This study presents a novel comparative analysis of the diagnostic performance of the General Temperament Questionnaire (GTQ) versus the Uterine Temperament Questionnaire (UTQ) within the framework of Persian Medicine. Our findings demonstrate substantial agreement between general and uterine temperament assessments while revealing important distinctions with significant clinical implications. The strong agreement between the GTQ and UTQ (κ = 0.72, p < 0.001) (Table 2 ) validates the fundamental PM principle of systemic-organ interconnection, suggesting that overall body temperament often reflects the status of vital organs( 16 , 21 ). This finding is particularly encouraging for PM practitioners as it supports the concept of systemic temperament affecting organ function( 16 , 19 ). The general temperament questionnaire can be a suitable alternative to the uterine temperament questionnaire and can assist in determining the uterine temperament of infertile women. The UTQ identified cold-wet temperament as the most prevalent type (50.0%), followed by cold-dry (40.32%) (Table 2 ), aligning with authoritative PM texts that consider coldness and wetness as predominant causes of infertility( 24 – 26 ). A crucial finding was the significant association between specific temperament patterns and infertility etiologies. The cold-wet uterine temperament showed strong association with PCOS, while cold-dry temperament was more common in primary ovarian insufficiency (POI) (Table 3 ). This correlation aligns with PM texts describing that women with cold-dry uterine temperament have reduced "quantity of semen" (eggs and associated secretions)( 24 , 25 ), corresponding to the modern understanding of POI( 30 , 31 ). This diagnostic insight suggests that PM-based dietary and medicinal interventions aimed at increasing semen quantity might potentially improve ovarian reserve in such women( 32 , 33 ), though clinical trials are needed to verify this hypothesis. The superior performance of the UTQ in organ-specific diagnosis is further supported by its stronger alignment with objective clinical biomarkers( 17 ). Both questionnaires revealed a significant relationship between higher BMI and wet temperament categories (cold-wet and warm-wet) (Table 4 ), connecting the PM diagnostic category of "wetness" with modern biomedical parameters of obesity. This association suggests that wetter temperaments may have greater susceptibility to overweight, obesity, and related conditions such as PCOS( 21 , 34 ). Although there is good agreement between the two questionnaires, it should also be noted that a 16% disagreement rate exists between them (10/62 cases, Table 2 ), While relatively small, this highlights the importance of organ-specific diagnosis. Relying solely on the GTQ may lead to diagnostic errors in approximately one-sixth of cases; therefore, the UTQ can be used to confirm the diagnosis when necessary in the management and treatment of infertile women. Limitations Several limitations should be acknowledged. The cross-sectional design prevents causal inference, and convenience sampling from a single center may limit generalizability. The absence of an independent gold standard, such as consensus diagnosis by PM experts, represents the most significant methodological limitation. Future studies should include such external validation and investigate whether therapeutic correction of uterine dystemperament leads to improved fertility outcomes. Conclusion Considering that both questionnaires show significant agreement, using the GTQ alongside the UTQ can assist in determining uterine temperament and facilitate the prevention, diagnosis, and treatment process of uterine disorders in infertile women. It can also support the confirmation of uterine temperament assessment by the UTQ. However, the UTQ demonstrates superior diagnostic accuracy at the organ-specific level and stronger correlation with clinical biomarkers. This study contributes to integrating traditional medicine diagnostic frameworks with contemporary biomedical knowledge and provides valuable insights for infertility treatment. Declarations Ethics approval and consent to participate The study protocol was reviewed and approved by the Ethics Committee of Mazandaran University of Medical Sciences (Ethical code: IR.MAZUMS.REC.1404.120). Written informed consent was obtained from all participants. The study adhered to institutional ethical standards and the Helsinki Declaration. Consent for publication Not applicable. This manuscript does not contain individual person’s data in any form. Availability of data and materials The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that they have no competing interests. Funding There is no funding to declare. Authors' Contributions F.F.R., S.S.Y., M.Z., and M.B.N. designed the study and conceptualized the manuscript. F.F.R., S.S.Y., and A.H. contributed to data acquisition, analysis, and interpretation. F.F.R., S.S.Y., and M.Z. wrote and revised the manuscript. M.B.N. and S.S.Y. supervised the project. All authors read and approved the final manuscript. Acknowledgments The authors would like to express their sincere gratitude to all the infertile women who participated in this study. We also extend our appreciation to the staff and physicians of the Infertility Clinic of Imam Khomeini Hospital, Sari, Iran, for their valuable cooperation and assistance in patient recruitment and data collection. Additionally, we would like to thank the research staff at the Traditional and Complementary Medicine Research Center, Mazandaran University of Medical Sciences, for their technical support and contributions to this project. 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Herbal foodstuffs in Avicenna’s recommended diet to improve sperm quality and increase male fertility; an evidence-based approach. J. Complement. Integr. Med. 19 (1), 47–70 (2022). Alibeigi, Z. et al. The impact of traditional medicine-based lifestyle and diet on infertility treatment in women undergoing assisted reproduction: a randomized controlled trial. Complement. Med. Res. 27 (4), 230–241 (2020). Murugan, M. et al. Genetic variants of leptin receptor gene (rs1137101) and obesity risk in prakriti individuals and its pathogenicity prediction using in silico approaches. Egypt. J. Med. Hum. Genet. 26 (1), 92 (2025). Additional Declarations No competing interests reported. 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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-7901926","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":596958128,"identity":"b6fef196-18e2-4acb-9e79-72e045debe56","order_by":0,"name":"Fazeleh Fazlollahpour-Rokni","email":"","orcid":"","institution":"Mazandaran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Fazeleh","middleName":"","lastName":"Fazlollahpour-Rokni","suffix":""},{"id":596958129,"identity":"7675290c-6ec7-4399-8195-66f66adde903","order_by":1,"name":"Seyede-Sedigheh Yousefi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYBADfjb2BiRuAhFaJNt4DpCqpUGCGGUgIN/AY/iY54+NBJ/k68SPP9u22TOwH37A8HAPbi0GB3iMjXnb0iTYpHM3S0i23U5s4EkzYEh4hkcLA4+ZNG/D4Tqglg0Shm23gc7LAfrlAF6HmUnz/PkvwSZ5dvOPxLbb9gz8b/BrYTgA0sJ2QIJNgnebxMG224wNEgRsMTjMVmw4ty1Zgo0nd5tlw7nbiW0SzwwO4HVYe/PGB2/+2EnIt5/dfPNH2W17fv7khw9/4HMYM4cBqgAbyLV4NAAB+wP88qNgFIyCUTAKAEkUSo+UPJzkAAAAAElFTkSuQmCC","orcid":"","institution":"Mazandaran University of Medical Sciences","correspondingAuthor":true,"prefix":"","firstName":"Seyede-Sedigheh","middleName":"","lastName":"Yousefi","suffix":""},{"id":596958131,"identity":"247d1198-9290-49ea-8d76-4c2bfaa42dc0","order_by":2,"name":"Marzieh Zamaniyan","email":"","orcid":"","institution":"Mazandaran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Marzieh","middleName":"","lastName":"Zamaniyan","suffix":""},{"id":596958132,"identity":"bb588218-60a7-4a41-9c4e-e58afc5295a5","order_by":3,"name":"Masoumeh Bagheri-Nesami","email":"","orcid":"","institution":"Mazandaran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Masoumeh","middleName":"","lastName":"Bagheri-Nesami","suffix":""},{"id":596958133,"identity":"4f568c34-32a1-43ca-a0cf-cf23f9e43c66","order_by":4,"name":"Abolfazl Hosseinnataj","email":"","orcid":"","institution":"Mazandaran University of Medical Sciences","correspondingAuthor":false,"prefix":"","firstName":"Abolfazl","middleName":"","lastName":"Hosseinnataj","suffix":""}],"badges":[],"createdAt":"2025-10-20 03:53:41","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7901926/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7901926/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103498725,"identity":"cdb788cc-9c47-429a-a5ff-21ca851a0a80","added_by":"auto","created_at":"2026-02-26 11:42:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":961119,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7901926/v1/992f52f0-7e2c-4a46-bd13-21156fd7e0fb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Diagnostic Power of General Temperament Questionnaire Compared to Uterine Temperament Questionnaire in Determining Uterine Temperament of Infertile Women: A Cross-Sectional Analytical Study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eInfertility is defined as the failure to achieve a clinical pregnancy after 12 months or more of regular unprotected sexual intercourse, or after six months for women over the age of 40(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). According to recent World Health Organization (WHO) reports, approximately 17.5% of the adult population\u0026mdash;affecting roughly one in six people worldwide\u0026mdash;experiences infertility(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Contributing factors include lifestyle changes, tobacco use, alcohol consumption, and obesity(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). In females, infertility most commonly results from ovulatory disorders, tubal and pelvic pathologies, and uterine or cervical abnormalities(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTreatment strategies for infertility vary based on the underlying etiology (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). For women with ovulation disorders, conventional treatments include ovulation induction using medications such as clomiphene citrate, injectable gonadotropins, and aromatase inhibitors like letrozole (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, these pharmacological interventions are associated with risks such as multiple pregnancies, ovarian hyperstimulation syndrome (OHSS), and a potential increase in the risk of ovarian and breast malignancies (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Assisted reproductive technologies (ART), particularly in vitro fertilization (IVF), represent another treatment avenue, though success rates remain limited with approximately two-thirds of cycles unsuccessful(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn recent decades, growing interest has emerged in complementary and alternative medicine approaches for managing infertility (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Persian Medicine (PM), with its historical foundation and holistic principles, serves as a complementary system that can be integrated with modern medicine to address gaps in disease prevention, health promotion, and treatment(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Central to PM is the concept of mizaj (temperament), which plays a determining role in prevention, treatment, and prognosis of diseases. Temperament influences emotional and physical characteristics as well as physiological functions(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Temperaments are classified into four singular types (hot, cold, dry, wet), four compound types (hot-wet, hot-dry, cold-wet, cold-dry), and a moderate type(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to PM theory, health reflects a state of temperamental balance, whereas dystemperament\u0026mdash;a deviation from the optimal temperament of an organ or the entire body\u0026mdash;can lead to functional impairment and disease (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Alterations in uterine temperament are among the most significant disorders related to the uterus in PM, affecting not only reproductive function but also the physiological activities of related organs (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). PM texts associate numerous gynecological conditions\u0026mdash;including infertility, recurrent miscarriage, oligomenorrhea, amenorrhea, menorrhagia, vaginitis, cervicitis, urinary incontinence, and pelvic pain\u0026mdash;with uterine dystemperament (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Accurate diagnosis and correction of uterine dystemperament may improve clinical outcomes, reduce the risk of treatment failure and recurrence, and decrease associated healthcare costs (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUterine temperament is assessed using a specialized questionnaire and diagnostic protocol based on clinical signs and uterine characteristics (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). PM literature describes the uterus as a vital organ whose dystemperament can influence overall health, potentially leading to systemic imbalance (\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Although a general temperament questionnaire has been developed and validated for assessing whole-body temperament in PM(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), and several studies have examined both general and uterine temperaments in gynecological disorders, no study has specifically evaluated the diagnostic accuracy of the general temperament questionnaire for determining uterine temperament in infertile women, or assessed the level of agreement between these two instruments (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn PM, the general temperament questionnaire is commonly used for diagnosing various diseases(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Given the holistic of PM, practitioners can often infer the temperament of specific organs from the overall body temperament(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). Therefore, if the general temperament questionnaire demonstrates adequate diagnostic accuracy in assessing uterine temperament compared to the dedicated uterine temperament questionnaire, it could streamline the diagnostic and therapeutic processes for uterine conditions and serve as a viable alternative to the uterine temperament questionnaire.\u003c/p\u003e \u003cp\u003eGiven the clinical relevance of uterine temperament diagnosis\u0026mdash;particularly in the context of infertility management\u0026mdash;and the comparative practicality of evaluating general versus uterine temperament, this study aimed to evaluate and compare the diagnostic accuracy of the General Temperament Questionnaire (GTQ) with the specialized Uterine Temperament Questionnaire (UTQ) for determining uterine temperament in infertile women. An accurate diagnosis of uterine temperament may facilitate early and preventive interventions, potentially reducing the risk of progression to irreversible infertility or disease recurrence. The findings may provide valuable insights for patients, PM specialists, gynecologists, and healthcare policymakers.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design and Setting\u003c/h2\u003e \u003cp\u003eThis cross-sectional analytical study was conducted at the Infertility Clinic of Imam Khomeini Hospital, a major referral center in Sari, Iran, from June 2025 to September 2025.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEthical Considerations\u003c/h3\u003e\n\u003cp\u003e The study protocol was reviewed and approved by the Ethics Committee of Mazandaran University of Medical Sciences (Ethical code: IR.MAZUMS.REC.1404.120). Written informed consent was obtained from all individual participants. The study procedures adhered to the ethical standards of the institutional research committee and the 1964 Helsinki Declaration and its subsequent amendments.\u003c/p\u003e\n\u003ch3\u003eParticipants\u003c/h3\u003e\n\u003cp\u003eA total of 62 infertile women were enrolled using a convenience sampling method. The inclusion criteria were: age between 18\u0026ndash;40 years, diagnosis of primary or secondary infertility due to ovarian causes or unexplained factors, and provision of informed consent. Exclusion criteria included: infertility due to anatomical factors (e.g., uterine fibroids, hydrosalpinx) or male factors, current tobacco use or alcohol consumption, history of any chronic underlying diseases, and use of hormonal medications that could influence temperament assessment.\u003c/p\u003e\n\u003ch3\u003eSample Size\u003c/h3\u003e\n\u003cp\u003eThe sample size was calculated as 58 participants using the formula for comparing a mean with a standard value, based on data from a previous study by Tansaz et al(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). With a mean general temperament score of 138.88\u0026thinsp;\u0026plusmn;\u0026thinsp;17.61 (σ), a significance level (α) of 0.05, a power (1-β) of 80%, and a margin of error (d) of 6.5, the calculation was as follows:\u003c/p\u003e \u003cp\u003en=(z1\u0026thinsp;\u0026minus;\u0026thinsp;α/2\u0026thinsp;+\u0026thinsp;z1\u0026thinsp;\u0026minus;\u0026thinsp;β)2\u0026thinsp;\u0026times;\u0026thinsp;σ2d2=(1.96\u0026thinsp;+\u0026thinsp;0.84)2\u0026times;(17.61)2(6.5)2\u0026thinsp;=\u0026thinsp;58n\u0026thinsp;=\u0026thinsp;d2(z1\u0026thinsp;\u0026minus;\u0026thinsp;α/2​+z1\u0026thinsp;\u0026minus;\u0026thinsp;β​)2\u0026thinsp;\u0026times;\u0026thinsp;σ2​=(6.5)2(1.96\u0026thinsp;+\u0026thinsp;0.84)2\u0026times;(17.61)2​=58\u003c/p\u003e \u003cp\u003eThe minimum sample size was determined to be 58 participants. To enhance data reliability and account for potential missing information, data were collected from 62 participants.\u003c/p\u003e\n\u003ch3\u003eInstruments and Data Collection\u003c/h3\u003e\n\u003cp\u003eData were collected using three tools:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eA Demographic and Clinical Information Questionnaire\u003c/b\u003e to record age, weight, height, body mass index, education, occupation, and cause of infertility.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe General Temperament Questionnaire (GTQ)\u003c/b\u003e: This validated 20-item instrument developed by Salmannejad et al(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). assesses overall body temperament (Cronbach's α\u0026thinsp;=\u0026thinsp;0.74). Its sensitivity (63\u0026ndash;80%) and specificity (57\u0026ndash;85%) have been established. Temperament is classified as Cold (score\u0026thinsp;\u0026le;\u0026thinsp;46), Moderate (47\u0026ndash;49), or Warm (\u0026ge;\u0026thinsp;50) for the first 15 items, and Wet (\u0026le;\u0026thinsp;14), Moderate (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), or Dry (\u0026ge;\u0026thinsp;17) for the remaining 5 items(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eThe Uterine Temperament Questionnaire (UTQ)\u003c/b\u003e: This validated 12-item instrument developed by Tansaz et al(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). specifically evaluates uterine temperament (Cronbach's α\u0026thinsp;\u0026gt;\u0026thinsp;0.70; content validity confirmed by experts). Scores for warmth/coldness (9 items) range from 9 (cold) to 63 (warm), with 36 as moderate. Scores for wetness/dryness (3 items) range from 3 (dry) to 21 (wet). Raw scores were normalized to a 1\u0026ndash;7 scale by dividing by the number of items in each subsection for final analysis(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eProcedure\u003c/h2\u003e \u003cp\u003eEligible participants were recruited from the infertility clinic using convenience sampling based on inclusion and exclusion criteria. Under the supervision of a Persian medicine specialist, participants completed the demographic questionnaire, GTQ, and UTQ during their clinic visit. All data were collected in a private clinical setting to ensure confidentiality and consistency.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eDescriptive statistics were presented using mean, standard deviation mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD), percentage (%), and frequency. The normality assumption was assessed using the Kolmogorov-Smirnov test. Agreement between questionnaires was assessed using Cohen's kappa coefficient (κ). Associations between temperament types and clinical variables were examined using appropriate statistical tests (chi-square for categorical variables, ANOVA for continuous variables based on distribution). Data analysis was performed using SPSS version 22. A p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant for all analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eParticipant Characteristics\u003c/p\u003e \u003cp\u003eA total of 62 infertile women with a mean age of 32.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5 years participated in the study. The majority of participants were housewives (67.7%), had a medium income level (52.5%), held a university education (63.9%), and were diagnosed with primary infertility (72.1%). The most common etiology of infertility was Polycystic Ovary Syndrome (PCOS), accounting for 52.6% of cases. The mean duration of marriage and infertility were 8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;3.9 and 5.2\u0026thinsp;\u0026plusmn;\u0026thinsp;3.3 years, respectively. The mean Body Mass Index (BMI) was 27.97\u0026thinsp;\u0026plusmn;\u0026thinsp;5.27 Who are categorized as overweight.. Detailed demographic and clinical characteristics of the participants are presented 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\u003eDemographic and Clinical Characteristics of Study Participants (n\u0026thinsp;=\u0026thinsp;62)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFrequency (N)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e67.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eIncome Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e39.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eInfertility Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEtiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePCOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e52.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnexplained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eSd\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMarriage_Duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eInfertility_Duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAgreement between the General Temperament Questionnaire (GTQ) and Uterine Temperament Questionnaire (UTQ)\u003c/p\u003e \u003cp\u003eThe frequency distribution of temperament types according to both questionnaires is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Based on the UTQ, the most prevalent uterine temperament was cold-wet (50.0%), followed by cold-dry (40.32%), hot-dry (6.45%), and hot-wet (3.23%). According to the GTQ, the most prevalent general temperament was cold-wet (53.23%), followed by cold-dry (37.09%), hot-wet (6.45%), and hot-dry (3.23%). The agreement between the two questionnaires, as assessed by Cohen\u0026rsquo;s Kappa coefficient, was 0.72 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. A kappa coefficient above 0.6 indicates a good level of agreement(\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e), and the obtained kappa of 0.72 signifies a strong and appropriate concordance between the two questionnaires. Therefore, the general body temperament questionnaire demonstrates adequate diagnostic accuracy compared to the uterine temperament questionnaire in diagnosing uterine temperament.\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\u003eCross-tabulation of Temperament Types Between Two Questionnaires\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemparement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eGeneral\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eUterine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCold-Dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHot-Dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCold-Wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHot-Wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003etotal (%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold-Dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25 (40.32)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot-Dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4 (6.45)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCold-Wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31 (50.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHot-Wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2 (3.23)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etotal (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (37.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (3.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e33 (53.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4 (6.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e62 (100.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e*Cohen\u0026rsquo;s κ\u0026thinsp;=\u0026thinsp;0.72 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)*\u003c/h2\u003e \u003cp\u003eAssociation of Temperament with Qualitative Variables\u003c/p\u003e \u003cp\u003eThe associations between temperament types (from both questionnaires) and qualitative variables are presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Chi-square analysis revealed that the distribution of temperament types was not significantly associated with job status, income level, education, or type of infertility (primary vs. secondary) for either questionnaire (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05 for all). However, a highly significant association was found between temperament and the etiology of infertility for both the UTQ and the GTQ (p\u0026thinsp;=\u0026thinsp;0.001 for both).\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\u003eAssociation Between Qualitative Variables and Temperament Types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eUterine Temperament\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003eGeneral Temperament\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecold- dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ehot-dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ecold- wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ehot- wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecold- dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003ehot-dry\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003ecold- wet\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003ehot- wet\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eOccupation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHousewife\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEmployee\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-employed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e0.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003e0.482\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eIncome Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e0.176\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBelow diploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ediploma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUniversity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e0.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003e0.761\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eInfertility Type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e0.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003e0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eEtiology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePCOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePOI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEndometriosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnexplained\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAssociation of Temperament with Quantitative Variables\u003c/p\u003e\u003cp\u003eThe relationship between quantitative variables and the different temperament categories is detailed in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. One-way ANOVA tests showed that variables such as age, marriage duration, and infertility duration were not significantly different across the various temperament categories for either questionnaire (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In contrast, BMI showed a statistically significant association with temperament types in both the UTQ (p\u0026thinsp;=\u0026thinsp;0.016) and the GTQ (p\u0026thinsp;=\u0026thinsp;0.004). Post-hoc analyses indicated that in both classifications, mean BMI was higher in cold-wet and hot-wet temperaments compared to cold-dry and hot-dry categories.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation Between Quantitative Variables and Temperament Types\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"12\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eUterine Temperament\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c12\" namest=\"c7\"\u003e \u003cp\u003eGeneral Temperament\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ecold- dry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehot-dry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ecold- wet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ehot- wet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ecold- dry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ehot-dry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003ecold- wet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003ehot- wet\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e34.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e30.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e35.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.558\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarriage Duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.946\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e8.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e7.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfertility Duration (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e5.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.525\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e27.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e28.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e25.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e29.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0.004\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c12\" namest=\"c12\"\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"},{"header":"Discussion","content":"\u003cp\u003eThis study presents a novel comparative analysis of the diagnostic performance of the General Temperament Questionnaire (GTQ) versus the Uterine Temperament Questionnaire (UTQ) within the framework of Persian Medicine. Our findings demonstrate substantial agreement between general and uterine temperament assessments while revealing important distinctions with significant clinical implications.\u003c/p\u003e \u003cp\u003eThe strong agreement between the GTQ and UTQ (κ\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) validates the fundamental PM principle of systemic-organ interconnection, suggesting that overall body temperament often reflects the status of vital organs(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). This finding is particularly encouraging for PM practitioners as it supports the concept of systemic temperament affecting organ function(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The general temperament questionnaire can be a suitable alternative to the uterine temperament questionnaire and can assist in determining the uterine temperament of infertile women. The UTQ identified cold-wet temperament as the most prevalent type (50.0%), followed by cold-dry (40.32%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), aligning with authoritative PM texts that consider coldness and wetness as predominant causes of infertility(\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eA crucial finding was the significant association between specific temperament patterns and infertility etiologies. The cold-wet uterine temperament showed strong association with PCOS, while cold-dry temperament was more common in primary ovarian insufficiency (POI) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). This correlation aligns with PM texts describing that women with cold-dry uterine temperament have reduced \"quantity of semen\" (eggs and associated secretions)(\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e), corresponding to the modern understanding of POI(\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). This diagnostic insight suggests that PM-based dietary and medicinal interventions aimed at increasing semen quantity might potentially improve ovarian reserve in such women(\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), though clinical trials are needed to verify this hypothesis.\u003c/p\u003e \u003cp\u003eThe superior performance of the UTQ in organ-specific diagnosis is further supported by its stronger alignment with objective clinical biomarkers(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Both questionnaires revealed a significant relationship between higher BMI and wet temperament categories (cold-wet and warm-wet) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), connecting the PM diagnostic category of \"wetness\" with modern biomedical parameters of obesity. This association suggests that wetter temperaments may have greater susceptibility to overweight, obesity, and related conditions such as PCOS(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAlthough there is good agreement between the two questionnaires, it should also be noted that a 16% disagreement rate exists between them (10/62 cases, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), While relatively small, this highlights the importance of organ-specific diagnosis. Relying solely on the GTQ may lead to diagnostic errors in approximately one-sixth of cases; therefore, the UTQ can be used to confirm the diagnosis when necessary in the management and treatment of infertile women.\u003c/p\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations should be acknowledged. The cross-sectional design prevents causal inference, and convenience sampling from a single center may limit generalizability. The absence of an independent gold standard, such as consensus diagnosis by PM experts, represents the most significant methodological limitation. Future studies should include such external validation and investigate whether therapeutic correction of uterine dystemperament leads to improved fertility outcomes.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eConsidering that both questionnaires show significant agreement, using the GTQ alongside the UTQ can assist in determining uterine temperament and facilitate the prevention, diagnosis, and treatment process of uterine disorders in infertile women. It can also support the confirmation of uterine temperament assessment by the UTQ. However, the UTQ demonstrates superior diagnostic accuracy at the organ-specific level and stronger correlation with clinical biomarkers. This study contributes to integrating traditional medicine diagnostic frameworks with contemporary biomedical knowledge and provides valuable insights for infertility treatment.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was reviewed and approved by the Ethics Committee of Mazandaran University of Medical Sciences (Ethical code: IR.MAZUMS.REC.1404.120). Written informed consent was obtained from all participants. The study adhered to institutional ethical standards and the Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This manuscript does not contain individual person\u0026rsquo;s data in any form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding to declare.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eF.F.R., S.S.Y., M.Z., and M.B.N. designed the study and conceptualized the manuscript. F.F.R., S.S.Y., and A.H. contributed to data acquisition, analysis, and interpretation. F.F.R., S.S.Y., and M.Z. wrote and revised the manuscript. M.B.N. and S.S.Y. supervised the project. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to express their sincere gratitude to all the infertile women who participated in this study. We also extend our appreciation to the staff and physicians of the Infertility Clinic of Imam Khomeini Hospital, Sari, Iran, for their valuable cooperation and assistance in patient recruitment and data collection. Additionally, we would like to thank the research staff at the Traditional and Complementary Medicine Research Center, Mazandaran University of Medical Sciences, for their technical support and contributions to this project.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003e\u003csup\u003e1\u003c/sup\u003eStudent Research Committee, Mazandaran University of Medical Sciences, Sari, Mazandaran, Iran. \u003csup\u003e2\u003c/sup\u003eSchool of Persian Medicine, Traditional and Complementary Medicine Research Center, Addiction Institute, Mazandaran University of Medical Sciences, Sari, Mazandaran, Iran.\u003csup\u003e\u0026nbsp;3\u003c/sup\u003eSexual and Reproductive Health Research Center, Mazandaran University of Medical Sciences, Sari, Mazandaran, Iran. \u003csup\u003e4\u003c/sup\u003eDiabetes Research Center, Mazandaran University of Medical Sciences, Sari, Mazandaran, Iran. \u003csup\u003e5\u003c/sup\u003eTraditional and Complementary Medicine Research Center, Addiction Institute, Mazandaran University of Medical Sciences, Sari, Mazandaran, Iran. \u003csup\u003e6\u003c/sup\u003eDepartment of Biostatistics and Epidemiology, School of Health, Mazandaran University of Medical Sciences, Sari, Mazandaran, Iran.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCox, C. et al. 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The impact of traditional medicine-based lifestyle and diet on infertility treatment in women undergoing assisted reproduction: a randomized controlled trial. \u003cem\u003eComplement. Med. Res.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e (4), 230\u0026ndash;241 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurugan, M. et al. Genetic variants of leptin receptor gene (rs1137101) and obesity risk in prakriti individuals and its pathogenicity prediction using in silico approaches. \u003cem\u003eEgypt. J. Med. Hum. Genet.\u003c/em\u003e \u003cb\u003e26\u003c/b\u003e (1), 92 (2025).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Infertility, Female, Uterine Temperament, Persian Medicine, Diagnostic Accuracy, Questionnaire","lastPublishedDoi":"10.21203/rs.3.rs-7901926/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7901926/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMizaj (temperament) is a fundamental concept in Persian medicine, critically influencing the prevention, treatment, and prognosis of diseases. The uterus, as a key reproductive organ, has its own specific temperament, which is assessed by a specialized questionnaire. Additionally, an individual\u0026rsquo;s general temperament may also reflect uterine temperament due to the holistic approach of Persian medicine.\u003c/p\u003e\u003ch2\u003eObjectives\u003c/h2\u003e \u003cp\u003eThis study aimed to evaluate the diagnostic accuracy of the General Temperament Questionnaire (GTQ) versus the Uterine Temperament Questionnaire (UTQ) for determining uterine temperament in infertile women.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis cross-sectional analytical study included 62 infertile women. Participants completed both the GTQ and UTQ. Diagnostic performance was assessed using sensitivity, specificity, and Cohen\u0026rsquo;s kappa (κ). Associations with clinical variables were analyzed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe UTQ identified cold-wet (50.0%) as the predominant uterine temperament, followed by cold-dry (40.32%). Strong agreement was found between the UTQ and GTQ (κ\u0026thinsp;=\u0026thinsp;0.72, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Both questionnaires showed significant association with infertility etiology (p\u0026thinsp;=\u0026thinsp;0.001), linking cold-wet temperament to Polycystic Ovarian Syndrome (PCOS) and cold-dry to primary ovarian insufficiency (POI). BMI was significantly higher in wet temperament categories (UTQ p\u0026thinsp;=\u0026thinsp;0.016, GTQ p\u0026thinsp;=\u0026thinsp;0.004).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eAlthough general and uterine temperament assessments show substantial agreement, the UTQ demonstrates stronger alignment with clinical biomarkers and provides more precise organ-specific diagnosis. Using the UTQ is recommended for targeted diagnosis of uterine dystemperament in infertile women to facilitate personalized treatment strategies and potentially prevent ineffective treatments in PM.\u003c/p\u003e","manuscriptTitle":"The Diagnostic Power of General Temperament Questionnaire Compared to Uterine Temperament Questionnaire in Determining Uterine Temperament of Infertile Women: A Cross-Sectional Analytical Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-26 11:40:20","doi":"10.21203/rs.3.rs-7901926/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-02-24T07:58:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-23T02:40:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-21T11:45:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-21T11:45:30+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-10-20T03:42:38+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4592ef77-756e-480f-bc53-ca7febaf4972","owner":[],"postedDate":"February 26th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":63522042,"name":"Health sciences/Diseases"},{"id":63522043,"name":"Health sciences/Health care"},{"id":63522044,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-02-26T11:40:21+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-26 11:40:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7901926","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7901926","identity":"rs-7901926","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}