Protocol for a systematic review of wearable devices for antenatal fetal monitoring
Bibliographic record
Abstract
Introduction: Fetal monitoring is a crucial component of antenatal care, facilitating early detection of fetal compromise and improving pregnancy outcomes. Traditional monitoring methods such as cardiotocography (CTG) and ultrasound are effective but primarily limited to clinical settings, requiring specialized expertise and resources. The rise of wearable medical devices and artificial intelligence (AI) applications presents an opportunity to enhance fetal monitoring by enabling continuous, real-time data collection outside clinical environments. These technologies have the potential to improve fetal health and obstetric outcomes, particularly in resource-limited settings. This systematic review aims to evaluate the use of wearable devices for antenatal fetal monitoring and their impact on fetal and obstetric outcomes. Methods and Analysis: This systematic review will adhere to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and the Synthesis Without Meta-analysis (SWiM) framework. A comprehensive search of PubMed, Embase, Cochrane Library, and Web of Science will be conducted to identify primary research studies investigating wearable devices designed for fetal monitoring during pregnancy. Studies will be included if they assess the effectiveness, accuracy, and clinical impact of wearable fetal monitoring devices. Primary outcomes will include markers of fetal well-being as well as neonatal and obstetric outcomes. Secondary outcomes will focus on patient experience and acceptability. Data extraction and quality assessment will be conducted independently by two reviewers using the National Institutes of Health (NIH) Quality Assessment Tool and the Newcastle-Ottawa Scale. A narrative synthesis will be performed to summarise the findings. Ethics and Dissemination: Ethical approval is not required since the study involves analysing published literature. The findings will be shared through peer-reviewed publications and conference presentations. This review will enhance the evidence base regarding the clinical utility of wearable fetal monitoring technologies and inform future research and device development. PROSPERO Registration: CRD4202348755 (current version 4.1).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.065 | 0.123 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.019 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.101 | 0.013 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".