Promoting a culture of prehabilitation for the surgical cancer patient
Bibliographic record
Abstract
BACKGROUND: Traditional rehabilitative approaches to perioperative cancer care have focused on the postoperative period to facilitate the return to presurgical baseline conditions. However, there is some realization that the preoperative period can be a very effective time for intervention as the patients are more amenable to target their physiological condition to prepare to overcome the metabolic cost of the surgical stress. METHODS: We undertook a narrative review of the current literature on surgical prehabilitation and discussed the current evidence of preoperative interventions before cancer surgery in order to increase physiological reserve before surgery and accelerate postoperative recovery. RESULTS: Published data indicate the positive impact of prehabilitation on postoperative functional capacity and return to daily activities. However, the current evidence on the impact on short- and long-term clinical outcome is limited, and more research needs to be conducted. CONCLUSION: Preliminary findings indicate that a group of interventions such as exercise, nutrition and anxiety reduction in the preoperative period can complement the enhanced recovery program and facilitate the return to baseline activities of daily living. It is not clear at this stage whether the preoperative increase in functional capacity mitigates the burden of postoperative morbidities and subsequent cancer therapies. Therefore, more research is warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".