SynthesisActa obstetricia et gynecologica Scandinavica2023
Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review.
Synthesis in Acta obstetricia et gynecologica Scandinavica, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 3 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed, 3 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Machine Learning-Based Prognostic Models for Functional Outcomes in Spinal Cord Injury: Systematic Review.JMIR medical informatics · 2026Pooled it
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods: a Korean translation.Ewha medical journal · 2025Guideline
- Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review.Acta obstetricia et gynecologica Scandinavica · 2023Pooled it
- Machine learning to predict adverse perinatal outcomes: a systematic review and meta-analysis.EClinicalMedicine · 2026Article
- Artificial Intelligence in Recurrent Pregnancy Loss: From Risk Prediction to ART Translation.Journal of clinical medicine · 2026Review
- Artificial intelligence in preterm birth prediction: a narrative review of current approaches and clinical applicability.Obstetrics & gynecology science · 2026Review
- AI-assisted point of care ultrasound and an innovative vaginal speculum: a proposed combined approach to reducing spontaneous preterm birth.Frontiers in medicine · 2026Article
- A machine learning-based risk prediction model for early preterm birth: development and prospective validation.Frontiers in medicine · 2026Article
- Secondary Prevention of AFAIS: Deploying Traditional Regression, Machine Learning, and Deep Learning Models to Validate and Update CHA2DS2-VASc for 90-Day Recurrence.Journal of clinical medicine · 2025Article
- Prediction of preterm birth from cervical length measurements in twin pregnancies using machine learning.Scientific reports · 2025Article
- Assessing the quality of reporting in artificial intelligence/machine learning research for cardiac amyloidosis.JAMIA open · 2025Article
- External Validation of a Model for Predicting Outcomes in Preterm Newborns.JAMA network open · 2025Article
- Machine learning prediction of preterm birth in women under 35 using routine biomarkers in a retrospective cohort study.Scientific reports · 2025Article
- PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods.BMJ (Clinical research ed.) · 2025Article
- Predicting preterm birth using machine learning methods.Scientific reports · 2025Article
- Application of artificial intelligence in risk assessment and management of venous thromboembolism: scoping review.Frontiers in physiology · 2025Review
- Article
- Cervical elastography: finding a novel predictor for improving the prediction of preterm birth in uncomplicated twin pregnancies.Archives of gynecology and obstetrics · 2024Article
- Evaluating the Efficacy of Cervical Tactile Ultrasound Technique as a Predictive Tool for Spontaneous Preterm Birth.Open journal of obstetrics and gynecology · 2024Article
- TRIPOD+AI statement: updated guidance for reporting clinical prediction models that use regression or machine learning methods.BMJ (Clinical research ed.) · 2024Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
introductionThere was limited evidence on the quality of reporting and methodological quality of prediction models using machine learning methods in preterm birth. This systematic review aimed to assess the reporting quality and risk of bias of a machine learning-based prediction model in preterm birth. MATERIAL AND
methodsWe conducted a systematic review, searching the PubMed, Embase, the Cochrane Library, China National Knowledge Infrastructure, China Biology Medicine disk, VIP Database, and WanFang Data from inception to September 27, 2021. Studies that developed (validated) a prediction model using machine learning methods in preterm birth were included. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement and Prediction model Risk of Bias Assessment Tool (PROBAST) to evaluate the reporting quality and the risk of bias of included studies, respectively. Findings were summarized using descriptive statistics and visual plots. The protocol was registered in PROSPERO (no. CRD 42022301623).
resultsTwenty-nine studies met the inclusion criteria, with 24 development-only studies and 5 development-with-validation studies. Overall, TRIPOD adherence per study ranged from 17% to 79%, with a median adherence of 49%. The reporting of title, abstract, blinding of predictors, sample size justification, explanation of model, and model performance were mostly poor, with TRIPOD adherence ranging from 4% to 17%. For all included studies, 79% had a high overall risk of bias, and 21% had an unclear overall risk of bias. The analysis domain was most commonly rated as high risk of bias in included studies, mainly as a result of small effective sample size, selection of predictors based on univariable analysis, and lack of calibration evaluation.
conclusionsReporting and methodological quality of machine learning-based prediction models in preterm birth were poor. It is urgent to improve the design, conduct, and reporting of such studies to boost the application of machine learning-based prediction models in preterm birth in clinical practice.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.