Development of composite outcomes for individual patient data (<scp>IPD</scp>) meta‐analysis on the effects of diet and lifestyle in pregnancy: a Delphi survey
Bibliographic record
Abstract
OBJECTIVE: To develop maternal, fetal, and neonatal composite outcomes relevant to the evaluation of diet and lifestyle interventions in pregnancy by individual patient data (IPD) meta-analysis. DESIGN: Delphi survey. SETTING: The International Weight Management in Pregnancy (i-WIP) collaborative network. Sample Twenty-six researchers from the i-WIP collaborative network from 11 countries. METHODS: A two-generational Delphi survey involving members of the i-WIP collaborative network (26 members in 11 countries) was undertaken to prioritise the individual outcomes for their importance in clinical care. The final components of the composite outcomes were identified using pre-specified criteria. MAIN OUTCOME MEASURES: Composite outcomes considered to be important for the evaluation of the effect of diet and lifestyle in pregnancy. RESULTS: Of the 36 maternal outcomes, nine were prioritised and the following were included in the final composite: pre-eclampsia or pregnancy-induced hypertension, gestational diabetes mellitus (GDM), elective or emergency caesarean section, and preterm delivery. Of the 27 fetal and neonatal outcomes, nine were further evaluated, with the final composite consisting of intrauterine death, small for gestational age, large for gestational age, and admission to a neonatal intensive care unit (NICU). CONCLUSIONS: Our work has identified the components of maternal, fetal, and neonatal composite outcomes required for the assessment of diet and lifestyle interventions in pregnancy by IPD meta-analysis.
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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 | 0.379 | 0.487 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.024 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".