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Record W4400698515 · doi:10.1016/j.jogc.2024.102611

Validation of Database Autopsy for Review of Pregnancy-Associated Deaths in Canada

2024· article· en· W4400698515 on OpenAlexafffundvenueabout
Susie Dzakpasu, Zhiping Li, Aimina Ayoub, Shu Qin Wei, Nathalie Auger

Bibliographic record

VenueJournal of Obstetrics and Gynaecology Canada · 2024
Typearticle
Languageen
FieldMedicine
TopicAutopsy Techniques and Outcomes
Canadian institutionsMcGill University Health CentreUniversité de MontréalInstitut National de Santé Publique du QuébecMcGill UniversityPublic Health Agency of Canada
FundersCanadian Institutes of Health ResearchFonds de Recherche du Québec - SantéPublic Health AgencyPublic Health Agency of Canada
KeywordsMedicineAutopsyDatabaseCause of deathPregnancyObstetricsInternal medicine

Abstract

fetched live from OpenAlex

The database autopsy method was developed to determine probable causes of maternal deaths in the Canadian Institute for Health Information's hospital discharge abstract database; however, the method has yet to be validated. Using immediate cause of death information from Québec's hospitalization database as the gold standard, this study assessed the validity and reliability of the database autopsy method for pregnancy-associated deaths. The method had high sensitivity and specificity for identifying the most common causes of these deaths, as well as high interobserver agreement. We conclude that the database autopsy method is valid and reliable overall.

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)0.000
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.018
GPT teacher head0.279
Teacher spread0.261 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2024
Admission routes4
Has abstractyes

Explore more

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