Routine functional testing after percutaneous coronary intervention
Bibliographic record
Abstract
BACKGROUND: It is unclear whether routine or selective functional testing is optimal following percutaneous coronary intervention (PCI) in high-risk patients. OBJECTIVES: The aim of this trial was to compare exercise endurance, functional status, and quality of life (QOL) among high-risk patients randomized to either routine or selective functional testing following PCI. METHODS: We randomized 84 patients to either routine or selective functional testing. Patients had one or more of the following: multivessel PCI, diabetes mellitus, left ventricular ejection fraction < 35%, and/or PCI of the proximal left anterior descending artery. Patients in the routine arm (n = 41) underwent maximum endurance exercise treadmill testing (ETT) with nuclear perfusion imaging at 1.5 and 6 months. Patients in the selective arm (n = 43) only underwent functional testing for a clinical indication. All patients underwent a maximum endurance ETT at 9 months. Exercise endurance, functional status, and QOL were assessed at 9 months. RESULTS: Most patients were middle-aged men (58 +/- 10 years old; 87% male) who underwent PCI with stenting (94%). Among routine functional testing patients, 27.0% and 41.9% had a positive functional test at 1.5 and 6 months, respectively. Exercise endurance was improved in the routine vs. selective arm at 9 months (metabolic equivalents: 10.3 +/- 2.6 vs. 8.6 +/- 3.0, P = 0.013). There was no difference in improvement from baseline for the Duke Activity Status Index, the Seattle Angina Questionnaire, or the SF-36. Nine-month cumulative incidences of cardiac procedures and clinical events were not significantly different. CONCLUSIONS: Routine functional testing following PCI in high-risk patients may lead to improved exercise endurance but not improved QOL.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".