Is It Time to Update How Suspected Angina Is Evaluated prior to the Use of Specialized Tests? Implications Based on a Systematic Review
Bibliographic record
Abstract
OBJECTIVES: Appropriate use of specialized tests to assess chest pain is based classically on minimal information such as age, gender and the patient's description of pain. This approach has not been reevaluated in decades. We examined the relationship between history, examination and routine laboratory tests to identify factors warranting prospective validation as predictors of underlying coronary artery disease (CAD). METHODS: Studies linking obstructive CAD (≥50% diameter stenosis of at least one vessel by invasive angiography or cardiac computed tomographic angiography) and elements of history, examination and laboratory tests were identified. RESULTS: Forty-one prospectively identified papers were analyzed. Advanced age, gender and chest pain descriptors were extremely important, although the last was less so in women, in whom the presence of risk factors may be more important. Physical examination and chest X-ray were largely noncontributory. Laboratory tests were of variable utility other than to identify risk factors not already known from the history. However, biomarkers such as troponin, brain natriuretic factor and inflammatory markers were promising. The electrocardiogram was mainly important for the identification of ST-T abnormalities. CONCLUSIONS: This review identifies the most promising factors warranting prospective validation for improving the pretest probability estimation of CAD, so appropriate use criteria for the utilization of specialized diagnostic tests can be updated and improved.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".