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Record W7100047574

Competing Interests: None declared.

2011· article· en· W7100047574 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicEmotional Intelligence and Performance
Canadian institutionsnot available
Fundersnot available
KeywordsConfidence intervalMaternal deathMortality rateInfant mortalityStandardized mortality ratioHealth statisticsMaternal morbidityPublic health
DOInot available

Abstract

fetched live from OpenAlex

Objectives: World Health Organization reports based on Canadian Vital Statistics data suggest a recent increase in maternal mortality rates in Canada. Since Vital Statistics data typically provide inaccurate estimates of maternal mortality, we examined temporal trends in Canada using hospitalization data. Methods: We identified in-hospital deaths among women aged 15 to 54 years from the Canadian Institute for Health Information’s hospitalization database from 1996–1997 to 2007–2008. Maternal deaths during delivery were identified, and other in-hospital deaths were linked with prior pregnancy/delivery hospitalization records. Maternal mortality rates, 95 % confidence intervals, and risk ratios (RRs) were estimated. Results: The maternal mortality rate in Canada was 9.2 per 100 000 deliveries (95 % CI 7.6 to 11.2) in 1996 to 1999 and 9.0 per 100 000

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 imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.815
Threshold uncertainty score0.621

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.1850.031

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 head0.191
GPT teacher head0.363
Teacher spread0.172 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2011
Admission routes1
Has abstractyes

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