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Record W4306386015 · doi:10.1016/j.cjca.2022.10.014

Identifying Very-Low-Risk Patients for Future Myocardial Infarction or Death

2022· article· en· W4306386015 on OpenAlexafffundvenueabout
Peter A. Kavsak, Joshua O. Cerasuolo, Mark Hewitt, Shawn Mondoux, Hsien Seow, Craig Ainsworth, Jinhui Ma, Andrew Worster, Dennis T. Ko

Bibliographic record

VenueCanadian Journal of Cardiology · 2022
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsInstitute for Clinical Evaluative SciencesMcMaster UniversityImpactMcMaster University Medical Centre
FundersCanadian Institutes of Health Research
KeywordsMedicineAcute coronary syndromeGuidelineMyocardial infarctionEmergency departmentChest painInternal medicineCardiologyClinical PracticeEmergency medicineIntensive care medicinePhysical therapyPathology

Abstract

fetched live from OpenAlex

High-sensitivity cardiac troponin (hs-cTn) testing has enabled shorter time intervals between serial measurements when assessing patients with possible acute coronary syndrome (ACS) with the use of a single sample strategy proposed for early risk stratification. Specifically, a rapid rule-out for myocardial infarction (MI) is suitable for a pathway if the sensitivity for 30-day cardiac events is ≥ 99%. At the population level (n = 131,095 emergency department [ED] patients), low hs-cTn results alone yielded sensitivities < 99%.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.003

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 head0.029
GPT teacher head0.300
Teacher spread0.270 · 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 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

Citations11
Published2022
Admission routes4
Has abstractyes

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