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Record W2084614330 · doi:10.1155/2013/623743

Maternal Deaths in NSW (2000–2006) from Nonmedical Causes (Suicide and Trauma) in the First Year following Birth

2013· article· en· W2084614330 on OpenAlexaff
Charlene Thornton, Virginia Schmied, Cindy‐Lee Dennis, Bryanne Barnett, Hannah Dahlen

Bibliographic record

VenueBioMed Research International · 2013
Typearticle
Languageen
FieldMedicine
TopicMaternal Mental Health During Pregnancy and Postpartum
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineAccidentalIncidence (geometry)PopulationPediatricsDemographyObstetricsEnvironmental health

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.059
GPT teacher head0.382
Teacher spread0.323 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations41
Published2013
Admission routes1
Has abstractyes

Explore more

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