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Record W4205215745 · doi:10.1016/j.cjca.2022.01.007

Prediction of Sudden Cardiac Arrest in the General Population: Review of Traditional and Emerging Risk Factors

2022· review· en· W4205215745 on OpenAlexafffundvenue
Andrew C.T. Ha, Barbara S. Doumouras, Chang Wang, Joan Tranmer, Douglas S. Lee

Bibliographic record

VenueCanadian Journal of Cardiology · 2022
Typereview
Languageen
FieldMedicine
TopicECG Monitoring and Analysis
Canadian institutionsUniversity of TorontoTed Rogers Centre for Heart ResearchQueen's UniversityUniversity Health Network
FundersCanadian Institutes of Health ResearchInstitute of Circulatory and Respiratory HealthUniversity Health NetworkUniversity of TorontoQueen's UniversityHeart and Stroke Foundation of Canada
KeywordsMedicinePopulationSudden cardiac deathCoronary artery diseaseSudden cardiac arrestDiseaseInternal medicineCardiologyEjection fractionIntensive care medicineHeart failureEnvironmental health

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0030.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.084
GPT teacher head0.312
Teacher spread0.227 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
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

Citations63
Published2022
Admission routes3
Has abstractno

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