Sarcopenia predicts short‐term treatment‐related toxicity in patients undergoing curative‐intent therapy for head and neck cancer: A systematic review and meta‐analysis
Bibliographic record
Abstract
Sarcopenia is an increasingly recognized biomarker associated with poorer outcomes. The objective of this study was to ascertain the effect of sarcopenia on treatment tolerance and short-term toxicity in head and neck cancer (HNC). A systematic review was performed using multiple databases. An inverse-variation, random-effects model was used to perform the meta-analysis to evaluate the effect of sarcopenia on severe treatment toxicity and poor treatment tolerance. Sixteen observational studies, including 3187 patients with HNC, were analyzed. The combined odds ratio (OR) for severe treatment toxicity and tolerance was 2.22 (95%CI 1.50-3.29) and 1.40 (95%CI 0.84-2.32), respectively. The effect of sarcopenia on short-term severe treatment toxicity was similar with upfront surgery (OR 2.03, 95%CI 1.22-3.37) and definitive radiotherapy (OR 2.24, 95%CI 1.18-4.27) Patients with sarcopenia are more than twice as likely to suffer a short-term treatment-related toxicity when undergoing curative-intent HNC treatment.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.025 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".