Development and validation of a prediction model on severe maternal outcomes among pregnant women with pre-eclampsia: a 10-year cohort study
Bibliographic record
Abstract
Pre-eclampsia is a severe hypertensive disorder of pregnancy and could lead to severe maternal morbidities and death. Our study aimed to develop and validate a prognostic prediction model for severe maternal outcomes among Chinese population with pre-eclampsia. We conducted a 10-year cohort study in a referral center by collecting all pregnant women who diagnosed as pre-eclampsia and delivered from 2005 to 2014. A composite of severe maternal outcomes, including maternal near-miss defined by World Health Organization, cortical blindness/retinal detachment, temporary facial paralysis and maternal death, were adopted. We used logistic regression model to develop Model 1 by retaining the predictors of p < 0.05, and further conducted Model 2 by adding quadratic terms and interaction terms to Model 1. We undertook a bootstrapping validation and estimated the model performance. A total of 397 pregnant women suffered from severe maternal outcomes among 2,793 eligible participants, with an incidence of 14.21% (95% confidence interval (CI) 12.91%-15.51%). Of 13 predictors were finally selected in Model 1. Combined with quadratic and interactive terms, the Model 2 showed higher area under the ROC curve (82.2%, 95% CI 79.6%-84.7%) and good calibration. By the bootstrapping validation, similar model performances were present.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".