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Abstract 253: Variability in the Rates of Normal Diagnostic Coronary Catheterizations and Non-invasive Pre-catheterization Cardiac Diagnostic Testing in Ontario

2015· article· en· W2268539546 on OpenAlexaffabout
Garth H. Oakes, Kori Kingsbury

Bibliographic record

VenueCirculation Cardiovascular Quality and Outcomes · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsOntario Stroke Network
Fundersnot available
KeywordsCath labMedicineCardiac catheterizationCoronary artery diseaseDisease registryEmergency medicineDiagnosis codeDiseaseCardiologyInternal medicineConventional PCIMyocardial infarctionPopulation

Abstract

fetched live from OpenAlex

The Cardiac Care Network of Ontario (CCN) serves as a system support to the Ministry of Health and Long Term Care as well as hospitals, and care providers in the province of Ontario, Canada. CCN is dedicated to improving the quality, efficiency, access and equity in the delivery of adult cardiovascular services in the province. CCN works to plan, coordinate, implement and evaluate adult cardiovascular care in Ontario, and is responsible for the CCN Cardiac Registry. Nineteen hospitals in Ontario perform diagnostic coronary catheterizations (CATH) and all enter data into the CCN Cardiac Registry. The objective of this analysis was to analyze temporal trends in the rates of CATH procedures across Ontario and examine in detail the rate of pre-CATH non-invasive cardiac diagnostic testing in patients who had normal or non-significant coronary artery disease (CAD) CATH results. CATH data in the CCN Cardiac Registry from patients with stable CAD in Ontario from fiscal years 2011/12 to 2014/15 were analyzed. Recognizing that non-elective CATH patients may present with acute symptoms that warrant immediate intervention and non-invasive cardiac diagnostic testing may not be appropriate, we excluded these patients from our analysis. We defined stable CAD as CATH patients assigned an urgency ranking of “Elective” or “Semi-Urgent” at the time of their procedure. From 2011/12 to 2014/15 the number of stable CAD CATH procedures performed in Ontario remained relatively unchanged, ranging between 29,000 and 30,000 procedures per year. The rate of CATH procedures in which the result was normal or non-significant CAD also remained stable, ranging from approximately 32 to 33.5% per year. Approximately 75% of all patients in this cohort received some type of pre-CATH non-invasive cardiac diagnostic test. Although these results remained relatively stable year-over-year at a provincial level, we identified considerable variation in these results between cardiac centres with centre-specific rates of normal CATHs ranging from 7% to 56% and the rate of pre-CATH diagnostic testing ranging from approximately 37% to 92%. While a proportion of CATH results are expected to be in the range of normal or non-significant CAD, the exact expected rate is undetermined. Our analysis revealed large variation in the rate of normal/non-significant CAD results between cardiac centres in Ontario, with several programs having a rate considerably higher than the provincial average. The appropriate use of non-invasive cardiac diagnostic testing in elective, stable patients prior to CATH requires additional evaluation as the quality of the diagnostic testing is uncertain. Equivocal and/or uncertain non-invasive diagnostic test results may prompt more patients with normal anatomy/non-significant disease to undergo CATH to render a definitive diagnosis. These present important opportunities for ongoing quality improvement.

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 score0.035
Threshold uncertainty score0.122

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
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 head0.065
GPT teacher head0.306
Teacher spread0.241 · 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".

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Citations0
Published2015
Admission routes2
Has abstractyes

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