Incidence and risk factors for impaired mobility in older cardiac surgery patients during the early postoperative period
Bibliographic record
Abstract
AIM: Mobility issues in the early postoperative period result in poor functional outcomes and diminished quality of life for patients of advanced age. We determined the incidence of and risk factors for mobility issues in the early postoperative period in patients receiving open heart cardiac surgery. METHODS: A retrospective chart review was carried out on 396 patients receiving open heart coronary artery bypass grafts (CABG), valve replacements and combination CABG-valve replacements in a tertiary care hospital. Data on demographics, comorbidities, laboratory values, medications, anesthesia and postoperative care were abstracted. Mobility issues were considered present if they were documented in the medical chart. All pre- and intraoperative variables were entered into logistic regression. RESULTS: The mean age was 66.4 ± 11.9 years. In a subset of patients aged 75 years and older, the mean age was 79.8 ± 3.7 years. Mobility issues affected 36.9% of individuals from the total sample, and 47.6% of older patients. Increased age was a weak predictor in the total sample (OR 1.03), but was the only predictor in older adults (OR 1.1). The strongest predictors in the total sample were preoperative COPD (OR 2.7), congestive heart failure (CHF; OR 2.1), renal disease (OR 1.9), and pre-existing physical impairment (OR 1.8). Older patients with mobility issues were more likely to be discharged to acute care facilities, and had higher rates of mortality 3 years after surgery. CONCLUSIONS: Over one-third of cardiac surgery patients experienced early postoperative mobility issues. Older patients and those with COPD, CHF, renal disease or pre-existing physical impairments might benefit from preoperative consultation with physical therapists.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".