Malnourished lung cancer patients have poor baseline functional capacity but show greatest improvements with multimodal prehabilitation
Bibliographic record
Abstract
OBJECTIVE: The objective is to characterize the presence of malnutrition, examine the association between malnutrition and baseline functional capacity (FC), and the extent to which patients benefit from preoperative multimodal prehabilitation in patients undergoing lung resection for cancer. METHODS: Data from 162 participants enrolled in multimodal prehabilitation or control before lung cancer surgery were analyzed. Malnutrition was measured using the Patient-Generated Subjective Global Assessment (PG-SGA) according to triage levels: low-nutrition-risk (PG-SGA 0-3), moderate-nutrition-risk (4-8) and high-nutrition-risk (≥9). Baseline differences in FC, measured by the 6-minute walk test (6MWT), were compared. Factorial analysis of covariance (ANCOVA) was conducted to examine the effect of nutrition status and intervention on mean change in 6MWT preoperatively. RESULTS: 51.2% patients were considered low-nutrition-risk, 37.7% moderate-nutrition-risk, and 11.1% high-nutrition-risk. Low-nutrition-risk patients had significantly higher 6MWT at baseline (mean of 484 m [standard deviation (SD) = 88]) compared with moderate-nutrition-risk (432 m [SD = 107], P = .005) and high-nutrition-risk groups (416 m [SD = 90], P = .022). The adjusted mean change in 6MWT between prehabilitation vs control was 18.1 (95% confidence interval, 3.8 to 32.3) vs 5.6 m (-14.1 to 25.4) in low-nutrition-risk (P = .309), 28.5 (11 to 46) vs -4 m (-31.3 to 23.4) in moderate-nutrition-risk (P = .053), and 58.9 (16.7 to 101.2) vs -39.7 m (-80.2 to 0.826) in high-nutrition-risk group (P = .001). CONCLUSIONS: Lung cancer patients at high-nutrition-risk awaiting surgery had significantly lower baseline FC compared with low-nutrition-risk patients but experienced significant improvements in preoperative FC upon receiving 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".