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Anesthesia-related Adverse Events in Obstetric Patients: A Population-based Study in Canada

2022· article· en· W4310552209 on OpenAlexaffabout
L. Baghirzada, D. Archer, A. Walker, M. Balki

Bibliographic record

VenueObstetric Anesthesia Digest · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac, Anesthesia and Surgical Outcomes
Canadian institutionsSouth Health CampusUniversity of Calgary
Fundersnot available
KeywordsMedicineObstetric anesthesiaAnesthesiaSpinal anesthesiaIncidence (geometry)ChildbirthAdverse effectPopulationRespiratory arrestPregnancy

Abstract

fetched live from OpenAlex

( Can J Anaesth . 2022;69:72–85) Anesthesia-related morbidity, while rare, is a useful indicator for obstetric care quality and patient safety during childbirth. Previous studies—including those correlating anesthesia complications with cardiac arrest, pre-existing conditions, living in rural areas, neuraxial block, respiratory arrest, and unrecognized spinal catheterization—focused on specific neuraxial anesthesia or specific complications instead of analyzing multiple complications within a large population. The aim of this study was to determine the incidence of anesthesia complications as well as the association of anesthesia-related adverse events and medical or obstetric conditions.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.341
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.006
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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

Citations0
Published2022
Admission routes2
Has abstractyes

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