Effect of a Preoperative Intervention on Preoperative and Postoperative Outcomes in Low-Risk Patients Awaiting Elective Coronary Artery Bypass Graft Surgery
Bibliographic record
Abstract
BACKGROUND: In publicly funded health care systems, a waiting period for such services as coronary artery bypass graft surgery (CABG) is common. The possibility of using the waiting period to improve patient outcomes should be investigated. OBJECTIVE: To examine the effect of a multidimensional preoperative intervention on presurgery and postsurgery outcomes in low-risk patients awaiting elective CABG. DESIGN: Randomized, controlled trial. SETTING: A regional cardiovascular surgery center in a tertiary care hospital, southwestern Ontario, Canada. PATIENTS: 249 patients on a waiting list for elective CABG whose surgeries were scheduled for a minimum of 10 weeks from the time of study recruitment. INTERVENTION: During the waiting period, the treatment group received exercise training twice per week, education and reinforcement, and monthly nurse-initiated telephone calls. After surgery, participation in a cardiac rehabilitation program was offered to all patients. MEASUREMENTS: Postoperative length of stay was the primary outcome. Secondary outcomes were exercise performance, general health-related quality of life, social support, anxiety, and utilization of health care services. RESULTS: Length of stay differed significantly between groups. Patients who received the preoperative intervention spent 1 less day [95% CI, 0.0 to 1.0 day] in the hospital overall (P = 0.002) and less time in the intensive care unit (median, 2.1 hours [CI, -1.2 to 16 hours]; P = 0.001). During the waiting period, patients in the intervention group had a better quality of life than controls. Improved quality of life continued up to 6 months after surgery. Mortality rates did not differ. CONCLUSION: The waiting period for elective procedures, such as CABG, may be used to enhance in-hospital and early-phase recovery, improving patients' functional abilities and quality of life while reducing their hospital stay.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".