Predictors of weight loss during radiotherapy in patients with stage I or II head and neck cancer
Bibliographic record
Abstract
BACKGROUND: The purpose of the study was to identify predictors of weight loss during radiotherapy (RT) in patients with stage I or II head and neck (HN) cancer. METHODS: This study was conducted as part of a phase 3 chemoprevention trial. A total of 540 patients were randomized. The patients were weighed before and after RT. Their baseline characteristics, including lifestyle habits, diet, and quality of life, were assessed as potential predictors. Predictors were identified using multiple linear regressions. The reliability of the model was assessed by bootstrap resampling. A receiver operating characteristics curve was generated to estimate the model's accuracy in predicting critical weight loss (>or=5%). RESULTS: The mean weight loss was 2.2 kg (standard deviation, 3.4). Five factors were associated with a greater weight loss: all HN cancer sites other than the glottic larynx (P<.001), higher pre-RT body weight (P<.001), stage II disease (P = .002), dysphagia and/or odynophagia before RT (P = .001), and a lower Karnofsky performance score (P = .028). There was no association with pre-RT lifestyle habits, diet, or quality of life. The bootstrapping method confirmed the reliability of this predictive model. The area under the curve was 71.3% (95% confidence interval, 65.8-76.9), which represents an acceptable ability of the model to predict critical weight loss. CONCLUSIONS: These results could be useful to clinicians for screening patients with early stage HN cancer treated by RT who require special nutritional attention.
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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) | 0.001 | 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 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".