Improving Labor Induction With Ultrasound: A Systematic Review

Improving Labor Induction With Ultrasound: A Systematic Review | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search Improving Labor Induction With Ultrasound: A Systematic Review Elisa Romero Callejas , Antonio Enamorado Plata , View ORCID Profile Miguel Ángel Luque Fernández , View ORCID Profile Juan Manuel Melchor Rodríguez , View ORCID Profile Miguel Ángel Montero Alonso doi: https://doi.org/10.1101/2025.01.30.25320795 Elisa Romero Callejas 1 University of Granada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Antonio Enamorado Plata 2 University of Granada Find this author on Google Scholar Find this author on PubMed Search for this author on this site Miguel Ángel Luque Fernández 3 Department of Statistics and Operations Research, Faculty of Medicine. University of Granada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Miguel Ángel Luque Fernández Juan Manuel Melchor Rodríguez 4 Department of Statistics and Operations Research, Faculty of Medicine. University of Granada, Instituto de Investigación Biosanitaria, ibs.GRANADA, Research “Unit Modelling Nature” (MNat), University of Granada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Juan Manuel Melchor Rodríguez Miguel Ángel Montero Alonso 5 Department of Statistics and Operations Research, Faculty of Medicine. University of Granada Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Miguel Ángel Montero Alonso For correspondence: mmontero{at}ugr.es Abstract Full Text Info/History Metrics Data/Code Preview PDF ABSTRACT Introduction Labor induction is performed when continuing the pregnancy poses a greater risk than ending it. Elastography is emerging as an increasingly relevant technique to predict the success of labor induction. Objective To evaluate the relevant literature regarding elastography as a predictive tool for the success of labor induction, resulting in an uncomplicated vaginal delivery. Methodology A systematic review was conducted on the predictive accuracy of elastography in labor induction, using PubMed, Scopus, and the Cochrane Library. A total of 10 studies were selected, covering the period from 2007 to 2022. The included studies were clinical trials and observational studies that analyzed elastography in predicting the success of induced labor. Success was defined as the completion of labor via vaginal delivery without maternal or neonatal complications. Results The elastography technique demonstrates an increased accuracy in predicting successful labor induction and vaginal delivery compared to the traditional use of Bishop score. Among other predictors of labor induction failure, defined as labor ending in a cesarean delivery, are: cervical length (Odds Ratio [OR] OR = 1.916; 95% Confidence Interval [1.451–2.530]), maternal age ≥35 years, and body mass index (BMI) ≥30 kg/m 2 (OR = 2.257; 95% CI [1.353–3.767]). However, parity decreases the probability of labor induction failure (OR = 0.129; 95% CI [0.063–0.265]). Discussion The reviewed scientific evidence suggests that the use of elastography before induction improves the chances of a successful labor induction. Therefore, the right moment for the labor induction based on a clinical decision should be supplemented with other objective methods such as the elastography helping to evaluate the potential success of the induction and therefore, helping to improve maternal and infant health outcomes. Further research is recommended on the comparative effectiveness of different methods to identify the optimal time for labor induction, as well as to validate its clinical utility. INTRODUCTION Labor induction involves stimulating uterine contractions before spontaneous labor begins, to achieve a vaginal delivery. It is usually recommended when continuing the pregnancy poses a greater risk than finishing it (add reference here). Even if the labor induction is recognized to be a safe technique, it could carry several risks for both the mother and the newborn baby. Therefore, proper indication in time, and risk assessment are crucial to reduce the likelihood of failure, possibly resulting in a cesarean delivery [ 1 – 3 ]. Labor induction has gained increasing relevance in recent years. The World Health Organization (WHO) recommends a population-based rate of 10% for induced labor, but many countries exceed this figure. In the United States, it surpasses 30% and is on the rise, while in Argentina and the United Kingdom, it reaches up to 20%, and in Canada, it stands at 21% (2,9,10). Predictive factors for induction success or failure include gestational age, body mass index (BMI), parity, and the Bishop score. The Bishop score constitute the most used tool for the prediction of induction success or failure [ 10 – 12 ]. One of the main components of the Bishop score is the cervical ripening. Cervical ripening is key in predicting the success of labor induction, as an immature cervix can lead to a cesarean delivery in over half of the cases [ 3 , 13 ]. While the use of the Bishop score is simple, it is also subjective and prone to inter-observer variability, as it relies solely on the palpation of imprecise points. Therefore, it is necessary to establish easily applicable and reproducible quantitative objective diagnostic criteria to evaluate the ripening and consistency of the cervix. The use of ultrasound elastography has been used as complement to decide the right moment for the labor induction [ 13 , 14 , 29 ]. We developed a systematic review of the literature aims to evaluate the scientific evidence to determine the comparative efficacy of different methods used to evaluate the success of failure of the labor induction, defined as the completion of labor via vaginal delivery without complications. We contrasted the use of the elastography, with the Bishop score and other predictive factors for the success or failure of labor induction. METHODS This systematic literature review focuses on evaluating the predictive accuracy of elastography in determining the success of labor induction in pregnant women undergoing induction. Vaginal delivery is considered the success criterion for assessing the predictive capacity of elastography. We conducted an extensive search in the PubMed, Scopus, and Cochrane Library databases, covering the period from August 2007 to January 202. We used as keywords: Labor Induction, Elastography, Ultrasound, Vaginal Delivery, and Bishop Score, while combining them with links like AND as well as OR. Following the search criteria and the selection process, a total of 182 studies were identified. The inclusion criteria were controlled clinical trials, as well as prospective and retrospective observational studies, investigating the use of elastography as an evaluation method in pregnant women undergoing labor induction. Studies that did not include vaginal delivery as the success indicator for labor induction were excluded. No language restrictions were applied. Figure 1 shows the flow of articles included in this review. Download figure Open in new tab Figure 1: Flowchart of the search and selection of the included literature. PRISMA Flow Diagram. Using the identified articles, we conducted a double-blind selection of articles to identify a total of 10 articles meeting the inclusion criteria for the review. 24 articles were excluded due to duplication. Of the remaining 158 articles, 136 were eliminated after reviewing the title, and 12 were excluded after reading the abstract [ 14 , 16 – 24 ]. From the 10 articles finally selected, relevant data were extracted, including the year of publication, the statistical methodology used, sample size, population characteristics, variables analyzed, study objectives, results obtained, conclusions, and methodology employed. RESULTS Table 1 shows the count of the occurrence of the most relevant variables mentioned above in the 10 studies selected for this review. The variables “Parity” and “Cervical Length” are the most frequently key words mentioned in the selected studies. View this table: View inline View popup Download powerpoint Table 1: Number of studies by the most frequent key words Table 2 presents all the extracted information from each of the 10 selected studies. In all these studies, the utility of cervical elastography for assessing and predicting the success of labor induction is emphasized, particularly in comparison to traditional methods such as the Bishop score and the cervical length measurement. Overall, these studies analyze the variables that can aid in predicting the failure or success of the induction technique. View this table: View inline View popup Table 2: Summary of the extracted information from each selected article, n=10 By observing the wide range of variables used in all the studies it can be seen how all studies aim to predict the success of labor induction based on the stiffness and cervical length of the cervix. However, to quantify these variables, each study uses its measurements, reflecting a heterogeneous variety regarding the study of cervical stiffness. To study the stiffness of the cervix, shear wave elastography is the device in use. For this purpose, a wide variety of measurement units are employed: Shear Wave Speed (SWS) in meters per second (m/s) [ 20 ], Cervical Shear Wave (CSW) measured in kilopascals (Kpa) [ 18 ], Cervical Tissue Strain (TS) [ 17 ], Angle of Progression (AOP) [ 21 ], Electrographic Score [ 21 ], Young’s Modulus: used to predict cervical dilation time and the risk of prolonged dilation after labor induction [ 22 ], Hardness Ratio (HR) [ 23 ], and Elastography Index, which is primarily used to predict the success of labor induction when using oxytocin [ 19 ]. The areas where cervical stiffness is measured are the internal and external cervical os. The basis for studying cervical rigidity is to measure the speed at which shear waves propagate through cervical tissue, indicating stiffness, the greater the propagation speed, the stiffer the tissue. A rigid tissue is considered immature, as it is less prepared to dilate at the time of labor. According to the previous statements, the lower the SWS/SWE, the softer and therefore more mature the tissue, which is more susceptible to indicate a successful labor induction. Therefore, patients with higher SWS/SWE have a greater risk of induction failure, either by not reaching the active phase of labor or by the need for cesarean delivery. In contrast, women with lower SWS/SWE have a higher success rate in labor induction. Tissue strain is greater in women who succeed in induction compared to those who do not achieve a successful induction. To study the cervical length of the cervix, a transvaginal ultrasound is used. All studies use cm or mm as units of measurement. The anatomical study site is the total length of the cervix, from the internal cervical to the external cervical. Although the length of the cervix varies in each woman, generally, shorter cervices tend to show greater maturity and capacity for dilation than longer cervices at the time of labor induction. Patients with a longer cervix have a higher risk of labor induction failure, either by not reaching the active phase of labor or by the need for cesarean delivery. Conversely, women with shorter cervices have a higher success rate in labor induction. The different measurement units employed in some of the selected articles are reflected in Table 3 . View this table: View inline View popup Table 3: Summary of the units of measurement used in some of the selected articles. DISCUSSION In this systematic review, cervical elastography is shown as an objective, safe, and promising tool to improve accuracy in assessing cervical maturation and, consequently, the success of vaginal induction. Despite the different methods used for cervical elastography, the results remained consistent, plus the conclusions were similar across various studies. Furthermore, the results indicated that cervical elastography is more precise than transvaginal ultrasound measurement of cervical length in predicting successful labor induction and superior to the Bishop score. Currently, there are two general approaches to imaging tissue elasticity using ultrasound. The first is deformation elastography, which evaluates tissue stiffness through images displayed in a color spectrum, showing how the tissue deforms under pressure. The second approach uses shear waves (SWEI) to assess tissue elasticity; it employs ultrasound beams to create and propagate shear waves through the tissue, with the speed of these waves relating to tissue stiffness. Both approaches provide a quantitative and objective assessment that can replace palpation in clinical practice. In this regard, Torres et al. (2022) [ 20 ] evaluates the preliminary capacity of elastography to provide quantitative and objective information about the condition of the cervix based on SWEI, establishing a prognosis for induction failure. These findings are further supported by Fruscalzo et al. (2015) [ 17 ], which demonstrated the potential utility of quantitative cervical elastography in predicting induction failure. Additionally, Muscatello et al. (2014) [ 25 ], in their multiple regression analysis for predicting cesarean delivery, confirmed that a stiffer cervix, assessed by cervical elastography, along with higher cervical length values, were good predictors for cesarean delivery. Additionally, this systematic literature review considers multiple variables that influence the prediction of success of a vaginal induction, thereby avoiding cesarean delivery. These variables include parity (multiparous women vs. nulliparous women), BMI, cervical length, Bishop score, cervical elasticity and stiffness, maternal age, and gestational age. Among all these variables, parity and the Bishop score stand out. The Bishop score is a classic and subjective technique for assessing cervical maturation, routinely used in obstetric clinical practice. Studies such as the one conducted by Lu et al. (2020) [ 18 ] affirm through univariate and multivariable analysis for predicting cesarean delivery that the probability of failure in labor induction decreases significantly in multiparous women and increases in women with greater cervical length as evaluated by the Bishop score. In some studies, such as the one by Zhou et al. (2021) [ 23 ], it has been observed that the Bishop score may have predictive value for the success of labor induction with a 95% confidence interval. In the systematic review conducted by Hatfield et al. (2007) [ 15 ], the Bishop score was compared with cervical length to predict success in induction across 10 studies. There were no significant differences in the diagnostic accuracy of both methods in successful induction, vaginal delivery, active labor, and vaginal delivery within the subsequent 24 hours [ 14 , 22 ]. However, in our review, we consider the subjective nature of the Bishop score, and therefore its difficulty in quantification and low reliability. Consequently, the use of the Bishop score as the sole predictive tool should be approached with caution, highlighting the importance of integrating other objective techniques to improve accuracy in assessing cervical maturation. Although it is a widely used method today due to its simple reproducibility, the Bishop score remains a subjective and imprecise method. Due to its nature, it does not provide us with valuable information and, therefore, should not be the only method for assessing cervical maturation. It is essential to complement it with other objective techniques, such as elastography, to achieve an objective and precise evaluation of cervical stiffness and elasticity. Currently, there is a growing use of artificial intelligence to improve the effectiveness of assessing cervical maturation in women undergoing induced labor by using data obtained through ultrasound and comparing it with evaluations conducted using the Bishop score. This involves the use of machine learning algorithms such as XGBoost, CatBoost, or RF (Random Forest), offering precise and objective evaluations of the predictive induction failure from the above-described covariates [ 30 , 31 ]. Among the limitations found in this review is the scarcity of existing literature when posing our research question in search engines, particularly during recent years. Furthermore, the available scientific evidence predominantly consists of observational studies, making it necessary to implement clinical trials and meta-analyses that reinforce the scientific evidence regarding the prediction of success in vaginal induction using novel, quantitative, and objective techniques such as elastography. Since our results clearly reflect the value of using elastography and its potential superiority over other traditionally used techniques, like the Bishop score or cervical length measurement, we consider it of utmost importance that elastography be implemented in clinical practice, either by combining it with or gradually replacing other traditional techniques already in use, in order to establish an appropriate protocol for the use of elastography in the same context as these other more subjective and less effective traditional techniques. The measurement of cervical stiffness through elastography and the assessment of cervical length in predicting the success of labor induction are innovative methods that have proven to be superior to the Bishop score, as they are objective and observer-independent, unlike the Bishop score. However, to determine how cervical stiffness is measured using elastography, while all studies agree that softer tissue favors labor induction and stiffer tissue leads to failure in labor induction, they fail to agree on a common measurement (there is a wide variety of measurement units). In contrast, the measurement of cervical length appears to be more consistent across all studies, with results indicating that shorter cervical lengths favor the success of labor induction. All studies illustrate areas under the curve that demonstrate how the incorporation of elastography and cervical length provides greater predictive value than the Bishop score, showing higher area under the curve, sensitivity, and specificity. In conclusion, this is a growing area of study, whose objectivity has already been demonstrated, but there is no standardized method for measuring tissue stiffness. It is crucial to increase the number of investigations to strengthen the clinical utility of elastography and find a common measurement unit. In addition to continuing to explore new approaches, as well as the potential integration of artificial intelligence—a field that has already begun to be explored with the use of ultrasound but is still in the very early stages of development—there could be opportunities to incorporate data specific to elastography, which is a completely unexplored area at present. The incorporation of artificial intelligence could represent a breakthrough by replacing more subjective and less precise traditional techniques, such as the Bishop score, with new effective, reproducible, and objective methods, thus improving maternal care in labor induction. CONCLUSIONS The analyzed studies indicate that elastography provides valuable information about the elasticity of the cervix and presents itself as a useful tool in predicting the success of labor induction. Despite the variety of measurement units used in these investigations, the consistency in the findings highlights its potential in clinical obstetrics practice. However, these studies also reveal the complexity and diversity of the factors that influence the labor induction process. Data Availability All data produced in the present work are contained in the manuscript Footnotes ( elisaromero{at}correo.ugr.es ) ( aenaplata{at}correo.ugr.es ) ( mluquefe{at}ugr.es ) ( jmelchor{at}ugr.es ) ( mmontero{at}ugr.es ) REFERENCES 1. ↵ Valenti E. Guías de manejo. Inducción al trabajo de parto . Revista del hospital materno infantil Ramón Sardá . 2002 ; 21 ( 2 ): 75 – 91 OpenUrl 2. Penfield CA , Wing DA . Labor Induction Techniques: Which Is the Best? Obstet Gynecol Clin North Am . 2017 ; 44 ( 4 ): 567 – 82 OpenUrl CrossRef PubMed 3. ↵ Inducción del parto (actualizado julio del 2013 ). Prog Obstet Ginecol . 2015 ; 58 ( 1 ): 54 – 64 . OpenUrl 4. Rábago J , Álvarez Bravo A , Castelazo Ayala L. Estado actual de la inducción médica del parto . Ginecol Obstet México . 2009 ; 77 ( 5 ): 250 – 8 OpenUrl 5. Zayas Fundora E , Alessandra Tome Díaz P. Albores y evolución de la Obstetricia . Universidad de ciencias médicas de La Habana . 2021 ; 60 ( 280 ): e918 OpenUrl 6. 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BMC Pregnancy Childbirth . 2023 Oct 18; 23 ( 1 ): 737 . doi: 10.1186/s12884-023-06023-4 . PMID: 37853378 ; PMCID: PMC10583473 . OpenUrl CrossRef PubMed 31. ↵ Hu T , Du S , Li X , Yang F , Zhang S , Yi J , Xiao B , Li T , He L. Establishment of a model for predicting the outcome of induced labor in full-term pregnancy based on a machine learning algorithm . Sci Rep . 2022 Nov 9; 12 ( 1 ): 19063 . doi: 10.1038/s41598-022-21954-2 . PMID: 36351938 ; PMCID: PMC9646791 . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted February 03, 2025. Download PDF Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. 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Share Improving Labor Induction With Ultrasound: A Systematic Review Elisa Romero Callejas , Antonio Enamorado Plata , Miguel Ángel Luque Fernández , Juan Manuel Melchor Rodríguez , Miguel Ángel Montero Alonso medRxiv 2025.01.30.25320795; doi: https://doi.org/10.1101/2025.01.30.25320795 Share This Article: Copy Citation Tools Improving Labor Induction With Ultrasound: A Systematic Review Elisa Romero Callejas , Antonio Enamorado Plata , Miguel Ángel Luque Fernández , Juan Manuel Melchor Rodríguez , Miguel Ángel Montero Alonso medRxiv 2025.01.30.25320795; doi: https://doi.org/10.1101/2025.01.30.25320795 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Obstetrics and Gynecology Subject Areas All Articles Addiction Medicine (568) Allergy and Immunology (863) Anesthesia (300) Cardiovascular Medicine (4435) Dentistry and Oral Medicine (444) Dermatology (382) Emergency Medicine (608) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1509) Epidemiology (15229) Forensic Medicine (30) Gastroenterology (1124) Genetic and Genomic Medicine (6600) Geriatric Medicine (668) Health Economics (997) Health Informatics (4536) Health Policy (1368) Health Systems and Quality Improvement (1613) Hematology (541) HIV/AIDS (1264) Infectious Diseases (except HIV/AIDS) (15916) Intensive Care and Critical Care Medicine (1103) Medical Education (623) Medical Ethics (146) Nephrology (667) Neurology (6599) Nursing (346) Nutrition (998) Obstetrics and Gynecology (1144) Occupational and Environmental Health (957) Oncology (3332) Ophthalmology (974) Orthopedics (369) Otolaryngology (420) Pain Medicine (436) Palliative Medicine (130) Pathology (663) Pediatrics (1693) Pharmacology and Therapeutics (691) Primary Care Research (711) Psychiatry and Clinical Psychology (5447) Public and Global Health (9231) Radiology and Imaging (2198) Rehabilitation Medicine and Physical Therapy (1370) Respiratory Medicine (1196) Rheumatology (593) Sexual and Reproductive Health (712) Sports Medicine (530) Surgery (712) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a00787eb8b140708',t:'MTc3OTU3NTY5Nw=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();