Performance of the fullPIERS model in predicting adverse maternal outcomes in pre‐eclampsia using patient data from the PIERS (Pre‐eclampsia Integrated Estimate of RiSk) cohort, collected on admission
Bibliographic record
Abstract
The fullPIERS (Pre-eclampsia Integrated Estimate of RiSk) model is a promising tool for the prediction of adverse outcomes in pre-eclampsia, developed using the worst values for predictor variables measured within 48 hours of admission. We reassessed the performance of fullPIERS using predictor variables obtained within 6 and 24 hours of admission, and found that the stratification capacity, calibration ability, and classification accuracy of the model remained high. The fullPIERS model is accurate as a rule-in test for adverse maternal outcome, with a likelihood ratio of 14.8 (95% CI 9.1-24.1) or 17.5 (95% CI 11.7-26.3) based on 6- and 24-hour data, respectively, for the women identified to be at highest risk (predicted probability ≥ 30%).
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 | 0.010 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".