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Record W4407204479 · doi:10.1016/j.cjca.2025.02.001

Evaluating Volume and Resource Needs for Implementing an ST-Elevation Myocardial Infarction Shock Protocol

2025· article· en· W4407204479 on OpenAlexafffundvenue
Rene Boudreau, Michel Le May, Nikita Malhotra, Cole Clifford, Quinton Barry, William Knoll, Morgane Laverdure, Taia Glover, Wryan HelmecziMD, Salar Farokhi Boroujeni, Marino Labinaz, Alexander Dick, Christopher Glover, Michael Froeschl, Zeeshan Ahmed, Omar Abdel‐Razek, Pietro Di Santo, Sharon Chih, Rebecca Mathew, Munir Boodhwani, Hadi Toeg, Brock Wilson, Aun‐Yeong Chong, Derek So

Bibliographic record

VenueCanadian Journal of Cardiology · 2025
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of TorontoMcMaster UniversityUniversity of Ottawa
FundersUniversity of Ottawa Heart Institute Foundation
KeywordsMedicineProtocol (science)Myocardial infarctionShock (circulatory)CardiologyInternal medicineElevation (ballistics)Resource (disambiguation)Intensive care medicineComputer networkPathologyAlternative medicine

Abstract

fetched live from OpenAlex

ST-elevation myocardial infarction (STEMI) complicated by cardiogenic shock (STEMI-CS) confers high mortality. Although temporary mechanical circulatory support (tMCS) may improve outcomes, previous studies evaluating intra-aortic balloon pump (IABP) and venoarterial extracorporeal oxygenation (VA-ECMO), including IABP-II and extracorporeal life support shock (ECLS-Shock), did not reduce mortality.1 The recent Danish-German Cardiogenic Shock (DanGer) trial demonstrated a 12.7% absolute mortality reduction using a microaxial flow pump (Impella CP [Abiomed, Danvers, MA]).

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)0.008
metaresearch head score (Gemma)0.022
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.044

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.036
GPT teacher head0.313
Teacher spread0.277 · 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

Citations1
Published2025
Admission routes3
Has abstractyes

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