What are the prognostic implications and factors relating to exercise induced electrocardiographic ST segment changes in the setting of a non-ischaemic stress echocardiogram?
Bibliographic record
Abstract
Background. Electrocardiographic (ECG) changes during stress testing are a common and perplexing finding during non-ischaemic stress echocardiography (SE). Research has provided conflicting results regarding the implications. Methods. SE was performed after maximal Bruce protocol treadmill exercise. Results. 3020 consecutive patients, mean age 58±12 years, 36% female, were followed-up for up to 9 years (mean 36±21 months) post SE. Time to first cardiac event (composite of heart failure admission, worsening New York Heart Association class, worsening ejection fraction, acute coronary syndrome, revascularization, angina or cardiovascular death) was analyzed and adjusted using Cox proportional hazards regression. Prognostic significance was found with 1.5mm of downsloping or horizontal ST depression. Adjusting for baseline differences, increased risk of composite major adverse cardiac events was shown with at least 1.5mm of exercise induced ST depression (Hazard ratio [HR] of 2.47, 95% Confidence ratio [CI] 1.67-3.72, p<0.0001). Patients achieving high level exercise capacity (≥13 metabolic equivalents or METs) with ST depression lower risk of cardiac events during follow-up Conclusion. Patients with ST segment depression but non-ischaemic stress imaging have poorer prognosis compared to patients with non-ischaemic stress echocardiograms with normal stress ECGs. ST depression of 1.5mm or more was established as a prognostically significance value. High exercise capacity improves prognosis, and ECG changes in that setting can be regarded as false positives. Overall, however, ST depression during non-ischaemic stress imaging is not a benign finding.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".