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Record W4415329893 · doi:10.14740/cr2143

Transfer and Survival of ST-Elevation Myocardial Infarction Medicare Patients

2025· article· en· W4415329893 on OpenAlexvenueno aff
Michelle Leeberg, Andrew Shermeyer, Michael J. Ward, Beth A Virnig, Julian Wolfson, Caitlin Carroll, Sayeh Nikpay

Bibliographic record

VenueCardiology Research · 2025
Typearticle
Languageen
FieldMedicine
TopicTrauma and Emergency Care Studies
Canadian institutionsnot available
FundersSociety for Academic Emergency Medicine
KeywordsMyocardial infarctionAffect (linguistics)Quality (philosophy)Electrocardiography in myocardial infarctionHeart failure

Abstract

fetched live from OpenAlex

Background: Interhospital transfer of ST-elevation myocardial infarction (STEMI) patients can lead to greater access to percutaneous coronary intervention (PCI) and reduce mortality. However, it is unclear how the characteristics of the transferring and receiving hospitals impacts mortality of transferred STEMI patients. Methods: In this retrospective cohort study, we estimated differences in mortality among STEMI patients undergoing interhospital transfer using Kaplan-Meier survival curves and adjusted hazard ratios derived from Cox proportional hazard models. Results: We found that partial PCI capability (i.e., retaining some patients while transferring others for PCI) of the transferring hospital and lower quality of the receiving hospital were associated with lower survival. Conclusions: Interhospital transfers driven by factors other than distance and quality can negatively affect patient outcomes.

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)0.007
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 score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
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 head0.058
GPT teacher head0.387
Teacher spread0.329 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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