Optimizing functional exercise capacity in the elderly surgical population
Bibliographic record
Abstract
PURPOSE OF REVIEW: There are several studies on the effect of exercise post surgery (rehabilitation), but few studies have looked at augmenting functional capacity prior to surgical admission (prehabilitation). A programme of prehabilitation is proposed in order to enhance functional exercise capacity in elderly patients with the intent to minimize the postoperative morbidity and accelerate postsurgical recovery. RECENT FINDINGS: Few studies have looked at exercise prehabilitation to improve functional capacity prior to surgical admission. Prehabilitation prior to orthopaedic surgery does not seem to improve quality of life or recovery. However, prehabilitation prior to abdominal or cardiac surgery, based on 275 elderly patients, results in fewer postoperative complications, shorter postoperative length of stay, improved quality of life, and reduced declines in functional disability compared to sedentary controls. SUMMARY: A concentrated 3-month progressive exercise prehabilitation programme consisting of aerobic training at 45-65% of maximal heart rate reserve (%HRR) along with periodic high-intensity interval training ( approximately 90% HRR) four times per week, 30-50 minutes per session, is recommended for improving cardiovascular functioning. A strength training programme of about 10 different exercises focused on large, multi-jointed muscle groups should also be implemented twice per week at a mean training intensity of 80% of one-repetition maximum. Finally, a minimum of 140 g ( approximately 560 kcal) of carbohydrate (CHO) should be taken 3 h before training to increase liver and muscle glycogen stores and a minimum of about 200 kcal of mixed protein-CHO should be ingested within 30 min following training to enhance muscle hypertrophy.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".