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Record W2983621108 · doi:10.1016/j.cjco.2020.12.007

Temporal Trends in in-Hospital Bleeding and Transfusion in a Contemporary Canadian ST-Elevation Myocardial Infarction Patient Population

2020· article· en· W2983621108 on OpenAlexafffundabout
Debraj Das, Anamaria Savu, Kevin R. Bainey, Robert C. Welsh, Padma Kaul

Bibliographic record

VenueCJC Open · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsCanadian VIGOUR CentreUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsMyocardial infarctionMedicineElevation (ballistics)Emergency medicinePopulationInternal medicineCardiologyMedical emergencyEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Although ST-elevation myocardial infarction (STEMI) management has evolved substantially over the past decade, its effect on bleeding and transfusion rates are largely unknown in a contemporary population. METHODS: Our study cohort included patients 20 years of age or older who were hospitalized for STEMI between 2007 and 2016 across all Canadian provinces, except Quebec. Unadjusted rates of bleeding and of transfusion during STEMI episodes were calculated overall and for each province according to fiscal year. Patients were stratified into 4 groups according to their bleeding/transfusion. Characteristics, treatment, and outcomes were compared between groups. Multivariate logistic regression modelling was used to assess the association between bleeding and transfusion on in-hospital mortality. RESULTS: < 0.0001]). The association remained after adjustment: bleeding (odds ratio [OR], 2.0; 95% confidence interval [CI], 1.8-2.4), transfusion (OR, 4.4; 95% CI, 3.9-4.9), and bleeding and transfusion (OR, 3.8; 95% CI, 3.2-4.6). CONCLUSIONS: The proportion of Canadian STEMI patients who experienced in-hospital bleeding and transfusion has decreased over the past 9 years. However, patients with bleed or transfusion remain at higher risk of adverse 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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.329
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.046
GPT teacher head0.321
Teacher spread0.275 · 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 teacher head, 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

Citations2
Published2020
Admission routes3
Has abstractyes

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