Severe maternal morbidity surveillance: Monitoring pregnant women at high risk for prolonged hospitalisation and death
Bibliographic record
Abstract
BACKGROUND: There is no international consensus on the definition and components of severe maternal morbidity (SMM). OBJECTIVES: To propose a comprehensive definition of SMM, to create an empirically justified list of SMM types and subtypes, and to use this to examine SMM in Canada. METHODS: Severe maternal morbidity was defined as a set of heterogeneous maternal conditions known to be associated with severe illness and with prolonged hospitalisation or high case fatality. Candidate SMM types/subtypes were evaluated using information on all hospital deliveries in Canada (excluding Quebec), 2006-2015. SMM rates for 2012-2016 were quantified as a composite and as SMM types/subtypes. Rate ratios and population attributable fractions (PAF) associated with overall and specific SMM types/subtypes were estimated in relation to length of hospital stay (LOS > 7 days) and case fatality. RESULTS: There were 22 799 cases of SMM subtypes (among 1 418 545 deliveries) that were associated with a prolonged LOS or high case fatality. Between 2012 and 2016, the composite SMM rate was 16.1 (95% confidence interval [CI] 15.9, 16.3) per 1000 deliveries. Severe pre-eclampsia and HELLP syndrome (514.6 per 100 000 deliveries), and severe postpartum haemorrhage (433.2 per 100 000 deliveries) were the most common SMM types, while case fatality rates among SMM subtypes were highest among women who had cardiac arrest and resuscitation (241.1 per 1000), hepatic failure (147.1 per 1000), dialysis (67.6 per 1000), and cerebrovascular accident/stroke (51.0 per 1000). The PAF for prolonged hospital stay related to SMM was 17.8% (95% CI 17.3, 18.3), while the PAF for maternal death associated with SMM was 88.0% (95% CI 74.6, 94.4). CONCLUSIONS: The proposed definition of SMM and associated list of SMM subtypes could be used for standardised SMM surveillance, with rate ratios and PAFs associated with specific SMM types/subtypes serving to inform clinical practice and public health policy.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".