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Record W4382982363 · doi:10.1177/1753495x231178405

How other countries can improve Canada's maternal mortality statistics

2023· review· en· W4382982363 on OpenAlexafffundabout
Kayvan Aflaki, Joel Ray

Bibliographic record

VenueObstetric Medicine · 2023
Typereview
Languageen
FieldMedicine
TopicGlobal Maternal and Child Health
Canadian institutionsSt. Michael's HospitalUniversity of Toronto
FundersPhysicians' Services Incorporated Foundation
KeywordsMedicinePregnancyConfidentialityMaternal deathMaternal morbidityMaternal healthMedical careFamily medicineDemographyMedical emergencyEnvironmental healthPopulationHealth services

Abstract

fetched live from OpenAlex

Maternal mortality is the death of a woman while pregnant or within 42 days of the end of pregnancy. Late maternal deaths are from 42 to 365 days thereafter. Maternal mortality is an important surrogate indicator of a woman's overall health, social and economic status, and the provision of antenatal and emergency obstetric care at regional and national levels. Canada does not have a national system to report on maternal mortality; rather, maternal death investigations fall under the legal purview of coroners and medical examiners within each individual province or territory. Furthermore, the Canadian Perinatal Surveillance System is limited by its access to a comprehensive dataset. Hence, there is no accurate national picture of mortality prevalence or trends. The implementation of a national confidential enquiry system is a crucial step toward detailing pregnancy and post-pregnancy maternal mortality in Canada and should be organized in accordance with existing successful international systems.

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)0.008
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.992
Threshold uncertainty score0.196

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.020
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.011
Science and technology studies0.0020.001
Scholarly communication0.0040.002
Open science0.0040.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0180.004

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 head0.057
GPT teacher head0.344
Teacher spread0.288 · 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
DomainReporting
GenreReview

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

Citations3
Published2023
Admission routes3
Has abstractyes

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