Pub Date : 2025-12-01DOI: 10.1016/j.ajogmf.2025.101783
Alison Verster MD, Madison M. Marcus PhD, Hannah Shadowen PhD, Nicole Boss MS, Amy Salisbury PhD, RN, Alison N. Goulding MD, MSCR, Caitlin E. Martin MD, MPH
{"title":"Postpartum buprenorphine continuation by initiation setting","authors":"Alison Verster MD, Madison M. Marcus PhD, Hannah Shadowen PhD, Nicole Boss MS, Amy Salisbury PhD, RN, Alison N. Goulding MD, MSCR, Caitlin E. Martin MD, MPH","doi":"10.1016/j.ajogmf.2025.101783","DOIUrl":"10.1016/j.ajogmf.2025.101783","url":null,"abstract":"","PeriodicalId":36186,"journal":{"name":"American Journal of Obstetrics & Gynecology Mfm","volume":"7 12","pages":"Article 101783"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.ajogmf.2025.101791
Ying Pang PhD., Weizhuo Wang M.D., Ping Yin M.D.
{"title":"Letter to the editor regarding “Antenatal dexamethasone vs betamethasone on glycemic control in mild gestational diabetes: A randomized clinical trial”","authors":"Ying Pang PhD., Weizhuo Wang M.D., Ping Yin M.D.","doi":"10.1016/j.ajogmf.2025.101791","DOIUrl":"10.1016/j.ajogmf.2025.101791","url":null,"abstract":"","PeriodicalId":36186,"journal":{"name":"American Journal of Obstetrics & Gynecology Mfm","volume":"7 12","pages":"Article 101791"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.ajogmf.2025.101797
Yang Zhang MD , Qingling Kang MD , Rirong Qu MD, PhD
{"title":"Letter to the editor regarding “Insight into the abnormal cardiotocographic patterns following neuraxial analgesia for pain management in labor”","authors":"Yang Zhang MD , Qingling Kang MD , Rirong Qu MD, PhD","doi":"10.1016/j.ajogmf.2025.101797","DOIUrl":"10.1016/j.ajogmf.2025.101797","url":null,"abstract":"","PeriodicalId":36186,"journal":{"name":"American Journal of Obstetrics & Gynecology Mfm","volume":"7 12","pages":"Article 101797"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145680804","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.ajogmf.2025.101807
Julia Whitley MD, Nandini Raghuraman MD, MSCI
{"title":"Reply to letter to the editor regarding “Postpartum diuretic administration and hospital readmission: a systematic review and meta-analysis”","authors":"Julia Whitley MD, Nandini Raghuraman MD, MSCI","doi":"10.1016/j.ajogmf.2025.101807","DOIUrl":"10.1016/j.ajogmf.2025.101807","url":null,"abstract":"","PeriodicalId":36186,"journal":{"name":"American Journal of Obstetrics & Gynecology Mfm","volume":"7 12","pages":"Article 101807"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-01DOI: 10.1016/j.ajogmf.2025.101800
Jesrine Hong MObGyn, Mukhri Hamdan PhD, Peng Chiong Tan PhD
{"title":"Reply to Letter: Optimized protocol for antenatal dexamethasone versus betamethasone in mild gestational diabetes mellitus","authors":"Jesrine Hong MObGyn, Mukhri Hamdan PhD, Peng Chiong Tan PhD","doi":"10.1016/j.ajogmf.2025.101800","DOIUrl":"10.1016/j.ajogmf.2025.101800","url":null,"abstract":"","PeriodicalId":36186,"journal":{"name":"American Journal of Obstetrics & Gynecology Mfm","volume":"7 12","pages":"Article 101800"},"PeriodicalIF":3.1,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145681451","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-28DOI: 10.1016/j.ajogmf.2025.101862
Giulia Zamagni MSc , Camilla Fregona MD , Moira Barbieri MD , Maria Sole Scalia MD , Lorenzo Monasta DSc , Christoph Lees MD, PhD , Tamara Stampalija MD, PhD , Giulia Barbati PhD
Objectives
Fetal growth restriction (FGR) significantly contribute to perinatal morbidity, mortality, and long-term adverse health outcomes. While small for gestational age (SGA) is often used as a proxy for FGR, it does not necessarily indicate pathological growth restriction. Given the increasing interest in machine learning (ML) for predicting FGR/SGA, this study systematically reviews ML applications in this domain, evaluating their methodological rigor and reporting quality, following standardized guidelines.
Data Sources
The systematic search was conducted in MEDLINE and Scopus on June 21, 2024, following PRISMA 2020 guidelines.
Study Eligibility Criteria
Eligible studies implemented ML models for FGR/SGA prediction using routinely available clinical variables and reported at least one area under the receiver operating characteristic (AUROC) and/or accuracy. Exclusions included preprints, conference abstracts, systematic reviews, animal studies, and models relying exclusively on biomarkers or genomics, as not part of the clinical practice.
Study Appraisal and Synthesis Methods
Two independent reviewers screened articles with the help of the Rayyan software. Risk of bias was assessed using the PROBAST checklist. Adherence to the guidelines on the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis+artificial intelligence (TRIPOD+AI) was evaluated across methods, results, and discussion sections using a 4-point Likert scale. Sample size adequacy was assessed for each study, accounting for outcome type, predictors, and outcome prevalence.
Results
The search identified 272 studies, with 20 meeting the inclusion criteria. Definitions of FGR/SGA were inconsistent, particularly in technical journals. Adherence to TRIPOD+AI guidelines was variable, as no model reported on fairness or heterogeneity across relevant subgroups, and only 15% reported on calibration. Only 30% of studies met the minimum sample size required for ML models, indicating potential overfitting and limited generalizability.
Conclusion
Despite the potential of ML models in predicting FGR/SGA, key limitations persist, including inconsistent outcome definitions, underpowered models, and suboptimal reporting of calibration and clinical applicability. Future studies should emphasize standardized definitions, robust sample sizes, and comprehensive reporting to enhance model reliability and clinical translation.
{"title":"Assessing adherence to TRIPOD+AI guidelines in machine learning models for predicting small for gestational age and fetal growth restriction: a systematic review","authors":"Giulia Zamagni MSc , Camilla Fregona MD , Moira Barbieri MD , Maria Sole Scalia MD , Lorenzo Monasta DSc , Christoph Lees MD, PhD , Tamara Stampalija MD, PhD , Giulia Barbati PhD","doi":"10.1016/j.ajogmf.2025.101862","DOIUrl":"10.1016/j.ajogmf.2025.101862","url":null,"abstract":"<div><h3>Objectives</h3><div>Fetal growth restriction (FGR) significantly contribute to perinatal morbidity, mortality, and long-term adverse health outcomes. While small for gestational age (SGA) is often used as a proxy for FGR, it does not necessarily indicate pathological growth restriction. Given the increasing interest in machine learning (ML) for predicting FGR/SGA, this study systematically reviews ML applications in this domain, evaluating their methodological rigor and reporting quality, following standardized guidelines.</div></div><div><h3>Data Sources</h3><div>The systematic search was conducted in MEDLINE and Scopus on June 21, 2024, following PRISMA 2020 guidelines.</div></div><div><h3>Study Eligibility Criteria</h3><div>Eligible studies implemented ML models for FGR/SGA prediction using routinely available clinical variables and reported at least one area under the receiver operating characteristic (AUROC) and/or accuracy. Exclusions included preprints, conference abstracts, systematic reviews, animal studies, and models relying exclusively on biomarkers or genomics, as not part of the clinical practice.</div></div><div><h3>Study Appraisal and Synthesis Methods</h3><div>Two independent reviewers screened articles with the help of the Rayyan software. Risk of bias was assessed using the PROBAST checklist. Adherence to the guidelines on the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis+artificial intelligence (TRIPOD+AI) was evaluated across methods, results, and discussion sections using a 4-point Likert scale. Sample size adequacy was assessed for each study, accounting for outcome type, predictors, and outcome prevalence.</div></div><div><h3>Results</h3><div>The search identified 272 studies, with 20 meeting the inclusion criteria. Definitions of FGR/SGA were inconsistent, particularly in technical journals. Adherence to TRIPOD+AI guidelines was variable, as no model reported on fairness or heterogeneity across relevant subgroups, and only 15% reported on calibration. Only 30% of studies met the minimum sample size required for ML models, indicating potential overfitting and limited generalizability.</div></div><div><h3>Conclusion</h3><div>Despite the potential of ML models in predicting FGR/SGA, key limitations persist, including inconsistent outcome definitions, underpowered models, and suboptimal reporting of calibration and clinical applicability. Future studies should emphasize standardized definitions, robust sample sizes, and comprehensive reporting to enhance model reliability and clinical translation.</div></div><div><h3>Video Abstract</h3><div><span><span><span><span><video><source></source></video></span><span><span>Download: <span>Download video (4MB)</span></span></span></span><span><span><p><span>Video</span>. </p></span></span></span></span></div></div>","PeriodicalId":36186,"journal":{"name":"American Journal of Obstetrics & Gynecology Mfm","volume":"8 2","pages":"Article 101862"},"PeriodicalIF":3.1,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145649119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-11-27DOI: 10.1016/j.ajogmf.2025.101857
Nahed O. ElHassan MD, MPH , Cain Farnam MS , Joseph W. Thompson MD, MPH , Nichole Stanley PhD , Corey J. Hayes PharmD, PhD , Chenghui Li PhD , Peter M. Mourani MD , Teresa J. Hudson PharmD, PhD , Robert Mcgehee Jr PhD , Bradley C. Martin PharmD, PhD
<div><h3>BACKGROUND AND OBJECTIVE</h3><div>Medical cannabis (MC) use has increased among pregnant women, yet little is known about the quantity, product types, and timing of use. This study conducted a population-level analysis of MC purchasing patterns among pregnant women in Arkansas.</div></div><div><h3>STUDY DESIGN</h3><div>This descriptive exploratory analysis examined MC purchasing among pregnant women in Arkansas from May 11, 2019 to August 31, 2022, following MC availability. Data from three statewide data sources, the MC Card Registry (patient and qualifying condition data), MC Dispensary Database (transactions and product details), and Birth Certificate Records (maternal demographics and obstetric diagnoses), were linked with the national Social Determinants of Health Database, which provided neighborhood-level socioeconomic indicators. Descriptive statistics summarized maternal and purchasing characteristics and identified factors associated with the amount of purchased delta-9-tetrahydrocannabinol (THC), cannabis main psychoactive ingredient.</div></div><div><h3>RESULTS</h3><div>Of the 72,992 pregnancies, 1185 (1.62%) included MC purchases during pregnancy. Among these, 774 (65.3%) were continuers, defined as those with MC purchases within 90 days prior to pregnancy that continued during pregnancy, and 411 (34.7%) initiated MC beginning during pregnancy. An additional 94 pregnancies purchased MC in the 90 days prior to pregnancy but discontinued by pregnancy onset. Compared to nonpurchasers, MC purchasers were more likely to be ≥30 years old (adjusted odds ratio (aOR)=1.34; <em>P</em><.0001) and tobacco smokers (aOR=1.64; <em>P</em><.0001) and less likely to be non-Hispanic Black (aOR=0.44; <em>P</em><.0001), married (aOR=0.68; <em>P</em><.0001), and privately insured (aOR=0.69; <em>P</em>=.0001). The mean (SD) daily THC purchased was 137.36 (170.04) mg/d. Continuers purchased 1.54 (95% CI, 1.32–1.78) times more daily THC during pregnancy than initiators and 3.84 (95% CI, 2.66–5.55) times more prepregnancy THC than discontinuers. The adjusted mean ratio (AMR) of daily THC was 1.82 (<em>P</em><.0001) times higher among continuers compared to initiators, and lower for women <20 years (AMR=0.52; <em>P</em>=.0021), privately insured (AMR=0.81; <em>P</em>=.0093), receiving prenatal care (AMR=0.72; <em>P</em>=.0420), residing in micropolitan (AMR=0.71; <em>P</em>=.0070) or small-town areas (AMR=0.67; <em>P</em>=.0095), or residing in neighborhoods with higher household food-stamp receipt (AMR=0.78; <em>P</em>=.0424).</div></div><div><h3>CONCLUSIONS</h3><div>The mean daily THC purchased during pregnancy exceeded therapeutic dosing ranges established for FDA-approved cannabinoid formulations in nonpregnant adults and was particularly high among women initiating MC purchase prepregnancy. These findings highlight the need to understand the risks of maternal MC exposure and the drivers to high-dose use, to better guide counseli
{"title":"From card to cradle: examining medical cannabis purchasing among pregnant women in Arkansas","authors":"Nahed O. ElHassan MD, MPH , Cain Farnam MS , Joseph W. Thompson MD, MPH , Nichole Stanley PhD , Corey J. Hayes PharmD, PhD , Chenghui Li PhD , Peter M. Mourani MD , Teresa J. Hudson PharmD, PhD , Robert Mcgehee Jr PhD , Bradley C. Martin PharmD, PhD","doi":"10.1016/j.ajogmf.2025.101857","DOIUrl":"10.1016/j.ajogmf.2025.101857","url":null,"abstract":"<div><h3>BACKGROUND AND OBJECTIVE</h3><div>Medical cannabis (MC) use has increased among pregnant women, yet little is known about the quantity, product types, and timing of use. This study conducted a population-level analysis of MC purchasing patterns among pregnant women in Arkansas.</div></div><div><h3>STUDY DESIGN</h3><div>This descriptive exploratory analysis examined MC purchasing among pregnant women in Arkansas from May 11, 2019 to August 31, 2022, following MC availability. Data from three statewide data sources, the MC Card Registry (patient and qualifying condition data), MC Dispensary Database (transactions and product details), and Birth Certificate Records (maternal demographics and obstetric diagnoses), were linked with the national Social Determinants of Health Database, which provided neighborhood-level socioeconomic indicators. Descriptive statistics summarized maternal and purchasing characteristics and identified factors associated with the amount of purchased delta-9-tetrahydrocannabinol (THC), cannabis main psychoactive ingredient.</div></div><div><h3>RESULTS</h3><div>Of the 72,992 pregnancies, 1185 (1.62%) included MC purchases during pregnancy. Among these, 774 (65.3%) were continuers, defined as those with MC purchases within 90 days prior to pregnancy that continued during pregnancy, and 411 (34.7%) initiated MC beginning during pregnancy. An additional 94 pregnancies purchased MC in the 90 days prior to pregnancy but discontinued by pregnancy onset. Compared to nonpurchasers, MC purchasers were more likely to be ≥30 years old (adjusted odds ratio (aOR)=1.34; <em>P</em><.0001) and tobacco smokers (aOR=1.64; <em>P</em><.0001) and less likely to be non-Hispanic Black (aOR=0.44; <em>P</em><.0001), married (aOR=0.68; <em>P</em><.0001), and privately insured (aOR=0.69; <em>P</em>=.0001). The mean (SD) daily THC purchased was 137.36 (170.04) mg/d. Continuers purchased 1.54 (95% CI, 1.32–1.78) times more daily THC during pregnancy than initiators and 3.84 (95% CI, 2.66–5.55) times more prepregnancy THC than discontinuers. The adjusted mean ratio (AMR) of daily THC was 1.82 (<em>P</em><.0001) times higher among continuers compared to initiators, and lower for women <20 years (AMR=0.52; <em>P</em>=.0021), privately insured (AMR=0.81; <em>P</em>=.0093), receiving prenatal care (AMR=0.72; <em>P</em>=.0420), residing in micropolitan (AMR=0.71; <em>P</em>=.0070) or small-town areas (AMR=0.67; <em>P</em>=.0095), or residing in neighborhoods with higher household food-stamp receipt (AMR=0.78; <em>P</em>=.0424).</div></div><div><h3>CONCLUSIONS</h3><div>The mean daily THC purchased during pregnancy exceeded therapeutic dosing ranges established for FDA-approved cannabinoid formulations in nonpregnant adults and was particularly high among women initiating MC purchase prepregnancy. These findings highlight the need to understand the risks of maternal MC exposure and the drivers to high-dose use, to better guide counseli","PeriodicalId":36186,"journal":{"name":"American Journal of Obstetrics & Gynecology Mfm","volume":"8 2","pages":"Article 101857"},"PeriodicalIF":3.1,"publicationDate":"2025-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145640730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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