Bibliographic record
Abstract
PURPOSE OF REVIEW: This review examines the diagnostic and prognostic performance of the standard exercise treadmill test (ETT) in comparison to stress imaging procedures. This topic is timely and relevant due to increasing healthcare expenditures and the substantially lower cost of the ETT. RECENT FINDINGS: The most important goal of noninvasive testing is to identify patients with left main or three-vessel coronary artery disease (CAD) or severely reduced left ventricular ejection fraction (LVEF) less than 35%. These patient subsets demonstrate a survival advantage when treated with coronary artery bypass grafting (severe CAD) or a defibrillator (LVEF <35%). This benefit is not present for patients with one-vessel or two-vessel CAD or preserved LVEF. For patients who have a normal resting ECG, studies have shown that the standard ETT is as accurate as imaging for identifying these high-risk patients. Outcome of patients with a low-risk exercise treadmill score is excellent. SUMMARY: The standard ETT should be the initial test in patients presenting for evaluation of CAD with the following characteristics: (1) ability to exercise adequately; (2) normal resting ECG; and (3) no prior revascularization. Applying this strategy should not sacrifice prognostic accuracy and should result in significant cost savings.
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 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.014 |
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".