Prehabilitation Program in Elderly Patients: A Prospective Cohort Study of Patients Followed Up Postoperatively for Up to 6 Months
Bibliographic record
Abstract
The preoperative period may be an opportune period to optimize patients’ physical condition with a multimodal preoperative program. The impact of a “prehabilitation” program on elderly patients is discussed. This mono-center observational cohort study included consecutively 139 patients planned for major abdominal and thoracic surgery, with 44 in the control group (age < 65) and 95 in the elderly group (age > 65). All patients followed a “prehabilitation” program including exercise training, nutritional optimization, psychological support, and behavioral change. Seventeen patients in the control group and 45 in the elderly group completed the study at six months. The 6-minute walk test (6 MWT) increased in both groups from the initial evaluation to the last (median value of 80 m (interquartile range 51) for those under 65 years; 59 m (34) for the elderly group; p = 0.114). The 6 MWT was also similar after one month of prehabilitation for both populations. The rate of postoperative complications was similar in the two groups. Prehabilitation showed equivalence in patients over 65 years of age compared to younger patients in terms of increase in functional capabilities and of postoperative evolution. This multimodal program represents a bundle of care that can benefit a frailer population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".