Maternal Deaths Using Coroner’s Data: A Latent Class Analysis
Bibliographic record
Abstract
OBJECTIVE: Knowledge regarding the antecedent clinical and social factors associated with maternal death around the time of pregnancy is limited. This study identified distinct subgroups of maternal deaths using population-based coroner's data, and that may inform ongoing preventative initiatives. METHODS: A detailed review of coroner's death files was performed for all of Ontario, Canada, where there is a single reporting mechanism for maternal deaths. Deaths in pregnancy, or within 365 days thereafter, were identified within the Office of the Chief Coroner for Ontario database, 2004-2020. Variables related to the social and clinical circumstances surrounding the deaths were abstracted in a standardized manner from each death file, including demographics, forensic information, nature and cause of death, and antecedent health and health care factors. These variables were then entered into a latent class analysis (LCA) to identify distinct types of deaths. RESULTS: Among 273 deaths identified in the study period, LCA optimally identified three distinct subgroups, namely, (1) in-hospital deaths arising during birth or soon thereafter (52.7% of the sample); (2) accidents and unforeseen obstetric complications also resulting in infant demise (26.3%); and (3) out-of-hospital suicides occurring postpartum (21.0%). Physical injury (22.0%) was the leading cause of death, followed by hemorrhage (16.8%) and overdose (13.3%). CONCLUSION: Peri-pregnancy maternal deaths can be classified into three distinct sub-types, with somewhat differing causes. These findings may enhance clinical and policy development aimed at reducing pregnancy mortality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".