Current CHS and NHBPEP Criteria for Severe Preeclampsia Do Not Uniformly Predict Adverse Maternal or Perinatal Outcomes
Bibliographic record
Abstract
OBJECTIVE: To determine the association between adverse maternal/perinatal outcomes and Canadian and U.S. preeclampsia severity criteria. METHODS: Using PIERS data (Preeclampsia Integrated Estimate of RiSk), an international continuous quality improvement project for women hospitalized with preeclampsia, we examined the association between preeclampsia severity criteria and adverse maternal and perinatal outcomes (univariable analysis, Fisher's exact test). Not evaluated were variables performed in <80% of pregnancies (e.g., 24-hour proteinuria). RESULTS: Few of the evaluated variables were associated with adverse maternal (chest pain/dyspnea, thrombocytopenia, 'elevated liver enzymes', HELLP syndrome, and creatinine >110 microM) or perinatal outcomes (dBP >110 mm Hg and suspected abruption) (at p < 0.01). CONCLUSIONS: In the PIERS cohort, most factors used in the Canadian or American classifications of severe preeclampsia do not predict adverse maternal and/or perinatal outcomes. Future classification systems should take this into account.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".