Maternal Deaths in NSW (2000–2006) from Nonmedical Causes (Suicide and Trauma) in the First Year following Birth
Bibliographic record
Abstract
INTRODUCTION: Trauma, including suicide, accidental injury, motor traffic accidents, and homicides, accounts for 73% of all maternal deaths (early and late) in NSW annually. Late maternal deaths are underreported and are not as well documented or acknowledged as early deaths. METHODS: Linked population datasets from births, hospital admissions, and death registrations were analysed for the period from 1 July 2000 to 31 December 2007. RESULTS: There were 552,901 births and a total of 129 maternal deaths. Of these deaths, 37 were early deaths (early MMR of 6.7/100,000) and 92 occurred late (late MMR of 16.6/100,000). Sixty-seven percent of deceased women had a mental health diagnosis and/or a mental health issue related to substance abuse noted. A notable peak in deaths appeared to occur from 9 to 12 months following birth with the odds ratio of a woman dying of nonmedical causes within 9-12 months of birth being 3.8 (95% CI 1.55-9.01) when compared to dying within the first 3 months following birth. CONCLUSION: Perinatal services are often constructed to provide short-term support. Long-term identification and support of women at particular risk of maternal death due to suicide and trauma in the first year following birth may help lower the incidence of late maternal deaths.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".