Navigating severe maternal morbidity using big data: Green, yellow, and red flags for researchers
Bibliographic record
Abstract
Severe maternal morbidity (SMM) is a concept initially developed to identify cases of near-miss, for quality of care audit. Definitions vary, but include medical conditions (e.g., acute myocardial infarction and sickle cell crisis), obstetric complications (e.g., eclampsia, amniotic fluid embolism), organ failure, and therapeutic interventions (e.g., hysterectomy, mechanical ventilation, massive transfusion). SMM has also become an endpoint for studies of maternal morbidity and mortality. As mortality is fortunately uncommon, this surrogate endpoint, comprising a composite of various life-threatening conditions and life-sustaining interventions, plays an important role in research and surveillance. SMM has become a composite of conditions (indicators) that are identifiable from health administrative databases. Unfortunately, like maternal death, SMM is gaining prevalence1—about 13 to 15 per 1000 births in Canada, and 11 to 16 per 1000 births in the US.2
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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 | 0.025 | 0.113 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.067 | 0.037 |
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".