Standardised Methods for Developing Conceptual Frameworks for Placental Disorders of Pregnancy: Pre‐Eclampsia and Stillbirth
Bibliographic record
Abstract
BACKGROUND: Risk factors for the placental disorders of pregnancy (pre-eclampsia, fetal growth restriction, preterm birth, and stillbirth) are complex, frequently involving the interplay between clinical factors and wider social and environmental determinants of health. Biomarkers modulate the maternal and fetal responses to biological processes that underlie the development of placental disorders. OBJECTIVES: To develop a standardised methodology to assess the importance of, and inter-relationships between, candidate risk factors for the various placental disorders. SEARCH STRATEGY: Systematic searches were conducted using Medline, Embase, Health Technology Assessments, Database of Abstracts of Reviews of Effects, Cochrane Library databases, Google Scholar, and reference lists of retrieved papers. SELECTION CRITERIA: We deployed a hierarchy of reviews, systematic reviews, and cohort studies with at least 1000 participants (100 for biomarker studies), published in the prior decade. DATA COLLECTION AND ANALYSIS: We assessed the strengths of association and quality of evidence linking risk factors with individual placental outcomes. CONCLUSIONS: We have developed a standardised approach to assess the importance and inter-relatedness of putative risk factors for the placental disorders of pregnancy.
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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 | 0.426 | 0.587 |
| Meta-epidemiology (narrow) | 0.007 | 0.005 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.071 | 0.048 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.013 | 0.020 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".