Randomized Assessment of Resource Use in Fast-track Cardiac Surgery 1-Year after Hospital Discharge
Bibliographic record
Abstract
BACKGROUND: The authors assessed the safety and resource use associated with fast-track cardiac anesthesia (FTCA) after coronary artery bypass graft surgery (CABG) over a 1-yr period. METHODS: One hundred twenty patients were initially randomized to FTCA (n = 60) or conventional anesthetic (n = 60) for primary elective CABG surgery. Patients were followed for 1-yr after index surgery through linkage to universal administrative databases. Acute care hospital readmission rates and length of stay (LOS) and the downstream use of health resources were compared. Resource use was analyzed as use of hospital and rehabilitation center bed-days, expenditures on physician services, and use of cardiac drugs. RESULTS: There were no deaths during the 1-yr follow-up after initial discharge; 15 (25%) patients from both groups were readmitted to acute care hospitals in the follow-up period. The mean LOS for acute care readmission was 0.3 (1.0) in the FTCA and 1.6 (6.3) days in the conventional group at 3 months; P= 0.01, 95% CI (0.1, 5.7) and 0.8 (1.8) and 2.9 (9.6) days at 12 months; P= 0.01, 95% CI (0.2, 7.5). Two (3.3%) patients in the FTCA group and 9 (15%) patients in the conventional group were transferred to rehabilitation facilities. The LOS was 0.3 (1.5) and 2.3 (5.7) days respectively; P= 0.001, 95% CI (0.6, 4.0). Specialist visits were more frequent in the FTCA group 6.2 (13.2) versus 1.9 (2.2) visits respectively; P= 0.002, 95% CI (-9.0, -1.3). Percentage reduction of FTCA cost was 68% at 3 months, P= 0.0002 and 49.5% at 1-yr, P= 0.004 after index hospital discharge. CONCLUSIONS: Fast-track cardiac anesthesia is a safe practice that decreases resource use for a 1-yr period after index hospitalization.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".