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Record W4409099356 · doi:10.1016/j.hlc.2025.02.102

National Heart Foundation of Australia & Cardiac Society of Australia and New Zealand: Comprehensive Australian Clinical Guideline for Diagnosing and Managing Acute Coronary Syndromes 2025

2025· article· en· W4409099356 on OpenAlexaboutno aff
David Brieger, Louise Cullen, Tom Briffa, Sarah Zaman, Ian Scott, Cynthia Papendick, Kimberley Bardsley, Angus Baumann, Alexandra Bennett, Robyn Clark, J. James Edelman, Sally C. Inglis, Lisa Kühn, Adam Livori, Julie Redfern, Hans G. Schneider, Liza Thomas, E. Wing‐Lun, Ling Zhang, E. Ho, Stacey Matthews

Bibliographic record

VenueHeart Lung and Circulation · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsnot available
FundersCardiac Society of Australia and New ZealandPercy Baxter Charitable TrustNational Heart Foundation of Australia
KeywordsMedicineGuidelineFoundation (evidence)Intensive care medicineAcute coronary syndromeCanadian Cardiovascular SocietyCardiologyPathologyMyocardial infarctionAngina

Abstract

fetched live from OpenAlex

National Heart Foundation of Australia & Cardiac Society of Australia and New Zealand: Comprehensive Australian Clinical Guideline for Diagnosing and Managing Acute Coronary Syndromes 2025

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)0.010
metaresearch head score (Gemma)0.037
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.165
Threshold uncertainty score0.328

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.037
Meta-epidemiology (narrow)0.0010.002
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0030.003
Science and technology studies0.0020.002
Scholarly communication0.0030.002
Open science0.0030.004
Research integrity0.0080.010
Insufficient payload (model declined to judge)0.0040.004

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 head0.150
GPT teacher head0.465
Teacher spread0.315 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations37
Published2025
Admission routes1
Has abstractno

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