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Record W4399071595 · doi:10.1016/j.cjca.2024.05.020

Current State of Invasive Coronary Function Testing in Canadian Catheterisation Laboratories: A National Survey

2024· article· en· W4399071595 on OpenAlexafffundvenueabout
Laurie‐Anne Boivin‐Proulx, Andrea Lavoie, Eric Schampaert, Aun‐Yeong Chong, Derek So, Olga Toleva, Javier Escaned, Steve Miner

Bibliographic record

VenueCanadian Journal of Cardiology · 2024
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsHôpital du Sacré-Cœur de MontréalSouthlake Regional Health CenterGenome PrairieUniversity of Ottawa
FundersUniversity of California, IrvineUniversity of Ottawa Heart Institute Foundation
KeywordsMedicineCoronary artery diseaseCardiologyInternal medicineCardiac catheterizationCoronary angiographyFractional flow reserveVasomotorMyocardial infarction

Abstract

fetched live from OpenAlex

Despite the availability of technologies and guidelines recommendations, invasive coronary function testing (IFT) remains underperformed in patients with ischemia with no obstructive coronary artery disease (INOCA), but the current state of IFT in Canadian cardiac catheterization laboratories (CCL) remained unknown. Therefore, we conducted an survey among Canadian CCL directors(n=46). While most CCL directors believed that IFT should be performed in INOCA patients experience persisting symptoms or adverse clinical outcomes, only 19.6% (n=9) and 30.4% (n=14) of responding centers performed invasive assessment of the microvasculature and vasomotor function, respectively.

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)0.002
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 score0.046
Threshold uncertainty score0.120

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.005
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.041
GPT teacher head0.292
Teacher spread0.251 · 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
Published2024
Admission routes4
Has abstractyes

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