Utility of routine exercise treadmill testing early after percutaneous coronary intervention
Bibliographic record
Abstract
BACKGROUND: There have been few prospective studies examining the utility of routine exercise treadmill testing (ETT) early after percutaneous coronary intervention (PCI). The objective of this study was to examine the impact of a routine ETT strategy early after PCI on follow-up cardiac events and procedures. METHODS: We examined 136 patients who underwent routine ETT at 6 weeks post-PCI in the ADORE trial. The ETT was classified as positive, indeterminate, or negative. The Duke Treadmill Score (DTS) was calculated for all patients. Follow-up occurred at 9 months. RESULTS: ETT results at 6 weeks were: 32 (23.5%) positive, 24 (17.6%) indeterminate and 80 (58.8%) negative. At 9 months, the composite event rate was 21.9% in those with a positive ETT, 20.8% in those with an indeterminate ETT and 12.5% in those with a negative ETT (p = 0.25 positive vs. negative ETT). The sensitivity of early ETT for predicting clinical events was 41.2%, the specificity was 73.3%, the positive predictive value was 21.9% and the negative predictive value was 87.5%. At 9 months, the cardiac procedure rate was 18.8% in those with a positive test, 13.0% in those with an indeterminate test, and 6.3% in those with a negative test (p = 0.07 positive vs. negative ETT). In a multivariate logistic regression model, coronary stenting during PCI and a >/= 85% MPHR achieved were found to be inversely associated with clinical events. However, the DTS did not independently predict clinical events. CONCLUSION: Although the statistical power of the study was limited by the small number of clinical events (particularly MI and death), the results of this study support the ACC/AHA guidelines that exercise treadmill testing should not be used routinely after PCI.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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".