Additional file 1 of External validation of prognostic models predicting pre-eclampsia: individual participant data meta-analysis
Bibliographic record
Abstract
Additional file 1: Supplementary methods: Additional details for handling missing data and evaluating predictive performance of models. Table S1: Search strategy for pre-eclampsia prediction models. Table S2: Predictors evaluated in the models externally validated in the IPPIC-UK cohorts. Table S3: Prediction models and equations identified from the literature search. Table S4: Study level characteristics of IPPIC-UK cohorts. Table S5: Patient characteristics of IPPIC-UK cohorts. Table S6: Number and proportion missing for each predictor in each cohort used for external validation. Table S7: Risk of bias assessment of the IPPIC-UK cohorts using the PROBAST tool. Table S8: Summary of linear predictor values and predicted probabilities for each model in each cohort. Table S9: Predictive performance statistics for models in the individual IPPIC-UK cohorts. Table S10: Predictive performance statistics for models in nulliparous women in all cohorts and in the POP cohort. Fig. S1: Decision curves for early pre-eclampsia models in SCOPE, UPBEAT and POP. Fig. S2: Decision curves for late pre-eclampsia models in SCOPE, Allen 2017, UPBEAT and POP.
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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 | 0.010 | 0.114 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.009 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.707 | 0.039 |
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".