Bibliographic record
Abstract
BACKGROUND: Although death rates are often used to monitor the quality of health care, in industrialized countries maternal deaths have become rare. Severe maternal morbidity has therefore been proposed as a supplementary indicator for surveillance of the quality of maternity care. Our purpose in this study was to describe severe maternal morbidity in Canada over a 10-year period, among women with or without major pre-existing conditions. METHODS: We carried out a retrospective cohort study of severe maternal morbidity involving 2,548,824 women who gave birth in Canadian hospitals between 1991 and 2000. Thirteen conditions that may threaten the life of the mother (e.g., eclampsia) and 11 major pre-existing chronic conditions (e.g., diabetes) that could be identified from diagnostic codes were noted. RESULTS: The overall rate of severe maternal morbidity was 4.38 per 1000 deliveries. The fatality rate among these women was 158 times that of the entire sample. Rates of venous thromboembolism, uterine rupture, adult respiratory distress syndrome, pulmonary edema, myocardial infarction, severe postpartum hemorrhage requiring hysterectomy, and assisted ventilation increased substantially from 1991 to 2000. The presence of major pre-existing conditions increased the risk of severe maternal morbidity to 6-fold. INTERPRETATION: Severe maternal morbidity occurs in about 1 of 250 deliveries in Canada, with marked recent increases in certain morbid conditions such as pulmonary edema, myocardial infarction, hemorrhage requiring hysterectomy, and the use of assisted ventilation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".