Clinical determinants of weight loss in patients receiving radiation and chemoirradiation for head and neck cancer: A prospective longitudinal view
Bibliographic record
Abstract
BACKGROUND: We aimed to determine the effects of systemic inflammation and symptoms of head and neck cancer patients on dietary intake and weight in relation to mode of treatment. METHODS: In all, 38 orally fed patients had intake, weight, C-reactive protein (CRP), and symptoms prospectively assessed at baseline, post-treatment, and follow-up. RESULTS: Intake/weight declined and CRP increased substantially in chemoirradiation patients (-11.4 ± 5.2 kg, -1214 kcal/day, 23.4 ± 24.9 mg/L; p < .05) versus radiotherapy patients (-3.5 ± 4.8 kg, -483 kcal/day, 8.3 ± 13.9 mg/L) during posttreatment (repeated-measures ANOVA). Multivariate generalized estimating equations modeling identified reduced swallowing capacity was a key predictor of energy intake in both treatment groups (p < .001); multiple symptoms experienced by radiotherapy/chemoirradiation patients were significant predictors of weight loss; additionally, in chemoirradiation patients, CRP was an independent predictor of weight loss (p < .001). CONCLUSIONS: Treatment of symptoms and systemic inflammation are important clinical targets to manage weight loss in patients with head and neck cancer, especially those treated with chemoirradiation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".