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Abstract 18360: Predictors of Physician Under-recognition of Angina in Outpatients With Stable Coronary Artery Disease

2015· article· en· W2899012347 on OpenAlexaboutno aff
Anna Grodzinsky, Mikhail Kosiborod, John F. Beltrame, Kensey Gosch, Philip G. Jones, John A. Spertus, Karen P. Alexander, Ali Shafiq, Suzanne V. Arnold

Bibliographic record

VenueCirculation · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiac Imaging and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineAnginaCoronary artery diseaseOdds ratioInternal medicineRevascularizationOddsCanadian Cardiovascular SocietyLogistic regressionPhysical therapyCardiologyMyocardial infarction

Abstract

fetched live from OpenAlex

Background: Under-recognition of angina by physicians may result in under-treatment with revascularization or medications that could improve patients’ quality of life. Patient and physician characteristics associated with under-recognition have never been described. Methods: Outpatients with stable CAD in a 24-site US registry completed the Seattle Angina Questionnaire (SAQ) and their physicians independently quantified patients’ angina in the month prior to their clinic visit. Angina frequency was categorized as none, monthly, and daily/weekly. Among patients who reported angina, under-recognition was defined as the physician reporting a lower frequency of angina than the patient. A hierarchical (for site and physician) logistic model examined patient and physician factors associated with under-recognition of angina. Physician variability was assessed with a median odds ratio (MOR), which compares the likelihood of 1 physician at 1 random site under-recognizing angina vs. another physician at another site. Results: Among 1203 patients with stable CAD, 304 patients reported angina in the prior month, of whom 122 (40%) were under-recognized by their physician. Physicians were more likely to under-recognize the frequency of angina in patients with heart failure and among patients with less frequent angina (Figure). No other patient or physician factors were associated with under-recognition. There was significant variability across physicians (MOR 2.6), indicating that some physicians were better than others at recognizing angina. Conclusions: Under-recognition of angina is common in routine clinical practice and was largely unrelated to standard patient and physician characteristics. The large variation across physicians suggests that a more systematic approach is needed to assess angina from patients with CAD. The use of a validated tool, such as the SAQ, should be tested for improving angina recognition and 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)0.010
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 score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.029
GPT teacher head0.252
Teacher spread0.223 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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