Retrospective validation study of miniPIERS prediction model in Zanzibar
Bibliographic record
Abstract
OBJECTIVE: To perform a retrospective external validation of miniPIERS in Zanzibar's referral hospital. METHODS: From February to December 2017, data were collected retrospectively on all cases of hypertensive disorders of pregnancy (HDP) admitted to Mnazi Mmoja Hospital, Zanzibar, Tanzania. The primary outcome was the predictive performance of miniPIERS by examining measures of discrimination, calibration, and stratification accuracy. The secondary outcome was the applicability of miniPIERS within the referral hospital setting. RESULTS: During this period, 2218 of 13 395 (21%) patients were identified with HDP, of whom 594 met the inclusion criteria. Sixty per cent of patients with adverse outcomes were excluded because they had experienced one of the adverse outcomes before admission. The discriminative ability of miniPIERS was inaccurate. It was not likely to aid risk stratification because of low sensitivity and low positive predictive value. The model showed fair discrimination in HDP before 34 weeks of gestation (area under the receiver operating characteristics curve 0.72, 95% confidence interval 0.63-0.82). CONCLUSIONS: The benefit of miniPIERS appeared to be limited, although clinical conditions make any validation challenging. Its application for risk stratification in preterm pregnancies should be further investigated.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".