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Record W3094020153 · doi:10.1161/jaha.120.017330

Use of Cardiac Noninvasive Testing After Emergency Department Discharge: Association of Hospital Network Testing Intensity and Outcomes in Ontario, Canada

2020· article· en· W3094020153 on OpenAlexaffabout
Idan Roifman, Lu Han, Maria Koh, Harindra C. Wijeysundera, Peter C. Austin, Pamela S. Douglas, Dennis T. Ko

Bibliographic record

VenueJournal of the American Heart Association · 2020
Typearticle
Languageen
FieldMedicine
TopicAcute Myocardial Infarction Research
Canadian institutionsHealth Sciences CentreUniversity of TorontoInstitute for Clinical Evaluative SciencesSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineEmergency departmentHazard ratioMyocardial infarctionChest painProportional hazards modelEmergency medicineInternal medicineCohort studyCohortRetrospective cohort studyConfidence interval

Abstract

fetched live from OpenAlex

Background The relationship between noninvasive cardiac diagnostic testing intensity and downstream clinical outcomes is unclear. Our objective was to examine the relationship between hospital network noninvasive cardiac diagnostic testing intensity and downstream clinical outcomes in patients who were discharged from the emergency department after assessment for chest pain. Methods and Results We employed a retrospective cohort study design of 387 809 patients evaluated for chest pain in the emergency department between April 1, 2010 and March 31, 2016. Hospital networks were divided into tertiles based on usage of noninvasive cardiac diagnostic testing. The primary outcome was a composite of acute myocardial infarction or all-cause mortality. Adjusted Cox proportional hazards models were used to compare the hazard of the composite outcome of myocardical infarction and/or all-cause mortality between the tertiles. After adjustment for clinically relevant covariates, patients evaluated for chest pain in intermediate noninvasive cardiac diagnostic testing usage tertile hospital networks did not have significantly different hazards of the composite outcome when compared with those evaluated in low usage tertile hospital networks >90 days (hazard ratio [HR], 1.00; 95% CI, 0.83-1.21), 6 months (HR, 1.07; 95% CI, 0.92-1.24), and 1 year (HR, 1.03; 95% CI, 0.94-1.14). Patients evaluated in the high usage tertile also did not have significantly different hazards of the composite outcome compared with those evaluated in the low usage tertile at 90 days (HR, 0.98; 95% CI, 0.80-1.19), 6 months (HR, 1.01; 95% CI, 0.87-1.17); and 1 year (HR, 0.95; 95% CI, 0.86-1.05). Conclusions Our population-based study demonstrated that high noninvasive cardiac diagnostic testing use intensity was not associated with reductions in downstream myocardial infarction or all-cause mortality.

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)0.003
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 score0.031
Threshold uncertainty score0.223

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.027
GPT teacher head0.271
Teacher spread0.244 · 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

Citations6
Published2020
Admission routes2
Has abstractyes

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