Poor Vitamin Status is Associated with Skeletal Muscle Loss and Mucositis in Head and Neck Cancer Patients
Bibliographic record
Abstract
Mucositis and muscle wasting are two common toxicity effects of cancer treatment in head and neck cancer (HNC). There is limited data evaluating cancer treatment toxicities in relation to vitamin status. This study aimed to assess changes in vitamin status during HNC treatment in relation to body composition, inflammation and mucositis. In this prospective cohort study, dietary intakes (3-day food record), plasma levels of vitamins and C-reactive protein (CRP) were assessed at baseline (at diagnosis) and post-treatment (after 6–8 weeks of radiation therapy with or without chemotherapy). Computed tomography images were used to quantify body composition. Mucositis information was collected from health records of patients. Twenty-eight HNC patients (age 60 ± 10 years) completed both study time points. Patients who developed mucositis had significantly lower dietary intake of vitamins and plasma 25-hydroxy vitamin D (25-OHD) and all-trans retinol levels (p < 0.02). Patients lost a considerable amount of muscle mass (3.4 kg) and fat mass (3.6 kg) over the course of treatment. There was a trend toward greater muscle loss in patients with 25-OHD < 50 nmol/L compared to patients with 25-OHD ≥ 50 nmol/L (p = 0.07). A significant negative correlation was found between plasma all-trans retinol and CRP level at the end of treatment (p = 0.03). Poor vitamin status could be a contributing factor in developing treatment-induced toxicities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".