Database Autopsy: An Efficient and Effective Confidential Enquiry into Maternal Deaths in Canada
Bibliographic record
Abstract
BACKGROUND: Maternal death surveillance in Canada relies on hospitalization data, which lacks information on the underlying cause of death. We developed a method for identifying underlying causes of maternal death, and quantified the frequency of maternal death by cause. METHODS: We used data from the Discharge Abstract Database for fiscal years 2013 to 2017 to identify women who died in Canadian hospitals (excluding Quebec) while pregnant or within 1 year of the end of pregnancy. A sequential narrative based on hospital admission(s) during and after pregnancy was constituted and reviewed to assign the underlying cause of death (based on the World Health Organization's framework). Maternal deaths (i.e., while pregnant or within 42 days after the end of pregnancy) and late maternal deaths (i.e., more than 42 days to a year after the end of pregnancy) were examined separately. RESULTS: We identified 85 maternal deaths. Direct obstetric causes included 8 deaths (9%) related to complications of spontaneous or induced abortion; 9 (11%), to hypertensive disorders of pregnancy; 15 (18%), to obstetric hemorrhage; 11 (13%), to pregnancy-related infection; 16 (19%), to other obstetric complications; and <5 (<6%), to complications of management. There were 21 (25%) maternal deaths with indirect obstetric causes, and <5 (<6%) with undetermined causes. Of 120 late maternal deaths, 16 (13%) had direct obstetric causes, among them, 9 deaths by suicide (56%). One hundred late maternal deaths (83%) had indirect obstetric causes; and <5 (<4%) had undetermined causes. CONCLUSIONS: The majority of maternal deaths in Canada have direct obstetric causes, whereas most late maternal deaths have indirect obstetric causes. Suicide is an important direct cause of late maternal death.
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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 | 0.061 | 0.145 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".