Pre‐rehabilitation interventions for patients with head and neck cancers: A systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To investigate the effect of pre-rehabilitation interventions such as nutrition and exercise for patients with head and neck cancer (HNC). METHODS: Web of Science, PubMed, Scopus, Google Scholar, and Cochrane databases were searched up to December 2022. Quality of life, length of hospital stay, postoperative complications, change in body mass index or muscle mass, and functional assessments were the primary outcomes. PRISMA guidelines were adhered to, and the study was registered on PROSPERO. The Cochrane Collaboration tool and Newcastle Ottawa scale assessed the quality of included studies. Pooled data are presented as odds ratios (OR) and 95% confidence intervals (CI). Analysis was conducted using RevMan5.4. RESULTS: A total of 31 articles were included for quantitative analysis and 15 for qualitative synthesis. Nutrition alone resulted in significant weight retention (2.60; 2.32, 2.88, p < 0.00001), length of stay (-4.00; -6.87, -1.13), p = 0.0006) and complications (0.64; 0.49, 0.83, p = 0.0009). Nutrition and psychoeducation resulted in a significant reduction in mortality rate (0.70; 0.49, 1.00, p = 0.05 and 0.60; 0.48, 0.74, p < 0.00001), and exercise resulted in a significant reduction in dysphagia (0.55; 0.35, 0.87, p = 0.01). Exercise with nutrition resulted in significant improvements in weight loss, length of stay, complications, and dysphagia. Randomized controlled trials (RCTs) had a moderate risk of bias and cohort studies were of fair to good quality. CONCLUSION: Prehabilitation programs based on exercise, nutrition, or psychoeducation demonstrated improved post-interventional outcomes in HNC, such as quality of life, and mortality and morbidity. Studies with longer follow-ups and larger sample sizes, and investigations comparing nutritional supplements with exercise programs are needed.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.033 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".