Mapping the Evidence on Unexplained Recurrent Pregnancy Loss to Inform Patient-Oriented Research Priorities: A Scoping Review Protocol
Bibliographic record
Abstract
Recurrent Pregnancy Loss (RPL) is a complex reproductive health condition with varying definitions across major medical societies. While RPL is widely acknowledged in clinical practice, more than 50% of all cases remain unexplained. Beyond its uncertain etiology, the experience of RPL is often shaped by inconsistencies in care across levels of the healthcare system as well as disparities in access to testing, follow-up, and psychosocial support. Although current clinical guidelines stress the importance of individualized care, many recommended treatments are empirical, with limited supporting evidence. Additionally, guidelines are often restricted to areas of consensus and provide limited direction for managing unexplained cases. The absence of a unifying cause for unexplained RPL limits the development of effective, targeted interventions and underscores the need to synthesize available literature to better understand the quality and scope of existing evidence. Given the emotional and clinical complexity of RPL, the high prevalence of unexplained cases, and the need for equitable, evidence-informed care, a scoping review is warranted. This method is particularly suited to the interdisciplinary nature of RPL research, which spans biomedical, psychological, epidemiological, and sociocultural domains. This scoping review is being undertaken to inform a research priority-setting initiative developed in partnership with the RPL Patient Advisory Board. By conducting this review, we aim to support the development of a more patient-centred and equitable research agenda that reflects the real-world concerns of individuals affected by RPL.
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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 | 0.127 | 0.148 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.051 | 0.010 |
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".