Identifying Selected Mental Health, Physical, and Social Determinants of Extended Length of Stay After Elective Cardiac Surgery
Bibliographic record
Abstract
OBJECTIVE: Cardiovascular disease is the second leading cause of death among Canadians, with approximately 40 000 open-heart surgeries such as coronary artery bypass grafting (CABG) and valve replacements performed annually. The best practices after cardiac surgery include a hospital stay of 4 to 7 days to facilitate safe discharge. We explored associations between an extended length of stay and selected mental, physical, and social determinants of health routinely collected before surgery, with the aim of informing the development of a future prescreening instrument to better identify patients at risk for prolonged recovery times. METHODS: A single-center longitudinal retrospective chart review was conducted at Trillium Health Partners, a large urban center in Canada. A list of 250 randomized charts, half with regular length of stay (LOS) (≤4 days) and half with extended LOS (>4 days), indicating those who underwent elective cardiac surgery between November 2021 and August 2023 was analyzed. Demographic, clinical, mental, physical, and social variables were examined to identify their associations with extended LOS. RESULTS: The study included 250 patients with a median age of 65 years (±12 years), 80% male, and 84% undergoing CABG. Factors associated with an extended LOS included cardiac surgeries other than CABG alone ( P = .002), female sex ( P = .038), left ventricular function >1 ( P = .024), need for postoperative homecare ( P = .028), and absence of a specialized medical home support program for patients' postcardiac surgery ( P = .017). CONCLUSIONS: The current in-hospital pathway of 4 days at our center may not be applicable to all patients undergoing elective cardiac surgery. These findings highlight the importance of considering sex, left ventricular function, need for homecare after surgery, and lack of specialized medical home support programs in preoperative screening and planning to improve resource allocation and patient outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".