External Validation of the fullPIERS Model for Predicting Adverse Maternal Outcomes in Pregnancy Hypertension in Low- and Middle-Income Countries
Bibliographic record
Abstract
The hypertensive disorders of pregnancy are leading causes of maternal mortality and morbidity, especially in low- and middle-income countries. Early identification of women with preeclampsia and other hypertensive disorders of pregnancy at high risk of complications will aid in reducing this health burden. The fullPIERS model (Preeclampsia Integrated Estimate of Risk) was developed for predicting adverse maternal outcomes from preeclampsia using data from tertiary centers in high-income countries and uses maternal demographics, signs, symptoms, and laboratory tests as predictors. We aimed to assess the validity of the fullPIERS model in women with the hypertensive disorders of pregnancy in low-resourced hospital settings. Using miniPIERS data collected on women admitted with hypertensive disorders of pregnancy between July 2008 and March 2012 in 7 hospitals in 5 low- and middle-income countries, the predicted probability of developing an adverse maternal outcome was calculated for each woman using the fullPIERS equation. Missing predictor values were imputed using multivariate imputation by chained equations. The performance of the model was evaluated for discrimination, calibration, and stratification capacity. Among 757 women with complete predictor data (complete-case analyses), the fullPIERS model had a good area under the receiver-operating characteristic curve of 0.77 (95% confidence interval, 0.72–0.82) with poor calibration ( P <0·001 for the Hosmer–Lemeshow goodness-of-fit test). Performance as a rule-in tool was moderate (likelihood ratio: 5.9; 95% confidence interval, 4.23–8.35) for women with ≥30% predicted probability of an adverse outcome. The fullPIERS model may be used in low-resourced setting hospitals to identify women with hypertensive disorders of pregnancy at high risk of adverse maternal outcomes in need of immediate interventions.
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 | 0.043 | 0.077 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| 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".