Prognostic utility of ischemic response in functional imaging tests (SPECT or stress echocardiography) in low-risk unstable angina patients
Bibliographic record
Abstract
BACKGROUND: The aim of this study is to determine the ability of ischemic response in imaging stress tests (single-photon emission computed tomography [SPECT] or stress echocardiography [SE]) to predict events in low-risk unstable angina patients. METHODS: Three hundred and fifty-nine patients with unstable angina (< 24 h), asymptomatic at admission, without ST-segment elevation or depression, normal troponins, and undergoing SPECT (n = 188) or SE (n = 171) during hospitalization (median = 1 day) were included. A positive imaging test (IMAGING+) was defined as the presence of reversible perfusion defects or wall motion abnormalities in at least 2 contiguous segments. Multivariate models were constructed using these results and clinical variables to predict events at 6 months. RESULTS: Ninety-nine (27%) patients had IMAGING+, 72/188 (38%) in SPECT and 27/17 (16%) in SE (p < 0.0001). Events occurred in 84 (23%) patients: 4 had myocardial infarction, 47 new hospitalizations due to angina and 33 coronary artery revascularizations. Independent predictors of coronary artery disease were: IMAGING+ (OR: 6.4, 95% CI: 3.4-11.8, p < 0.0001), history of coronary artery disease (OR: 2.5, 95% CI: 1.2-5.2, p < 0.02) and TIMI risk (OR: 1.5, 95% CI: 1.1-2.2, p < 0.03). CONCLUSIONS: In low-risk unstable angina patients, an ischemic response in functional stress tests (SPECT or SE) was associated with adverse events and severe coronary artery disease.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".