Malnutrition modifies the response to multimodal prehabilitation: a pooled analysis of prehabilitation trials
Bibliographic record
Abstract
Patients with colorectal cancer are at risk of malnutrition before surgery. Multimodal prehabilitation (nutrition, exercise, stress reduction) readies patients physically and mentally for their operation. However, it is unclear whether extent of malnutrition influences prehabilitation outcomes. We conducted a pooled analysis from five 4-week multimodal prehabilitation trials in colorectal cancer surgery (prehabilitation: n = 195; control: n = 71). Each patient’s nutritional status was evaluated at baseline using the Patient-Generated Subjective Global Assessment (PG-SGA; higher score, greater need for treatment of malnutrition). Functional walking capacity was measured with the 6-minute walk test distance (6MWD) at baseline and before surgery. A multivariable mixed effects logistic regression model evaluated the potential modifying effect of PG-SGA on a clinically meaningful change of ≥19 m in 6MWD before surgery. Multimodal prehabilitation increased the odds by 3.4 times that colorectal cancer patients improved their 6MWD before surgery as compared with control (95% confidence interval (CI): 1.6 to 7.3; P = 0.001, n = 220). Nutritional status significantly modified this outcome (P = 0.007): Neither those patients with PG-SGA ≥9 (adjusted odds ratio: 1.3; 95% CI: 0.23 to 7.2, P = 0.771, n = 39) nor PG-SGA <4 (adjusted odds ratio: 1.3; 95% CI: 0.5 to 3.8, P = 0.574, n = 87) improved in 6MWD with prehabilitation. In conclusion, baseline nutritional status modifies prehabilitation effectiveness before colorectal cancer surgery. Patients with a PG-SGA score 4–8 appear to benefit most (physically) from 4 weeks of multimodal prehabilitation. Novelty: Nutritional status is an effect modifier of prehabilitation physical function outcomes. Patients with a PG-SGA score 4–8 benefited physically from 4 weeks of multimodal prehabilitation.
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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 | 0.012 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.022 |
| Bibliometrics | 0.001 | 0.002 |
| 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".