The effect of body mass index at cancer diagnosis on survival of patients with squamous cell head and neck carcinoma
Bibliographic record
Abstract
Abstract The aim of this study is to investigate the prognostic role of body mass index (BMI) and survival from head and neck cancer (HNC). We performed a pooled analysis of studies included in the International Head and Neck Cancer Epidemiology consortium in order to investigate the prognostic role of BMI and survival from HNC. We used Cox proportional hazards models to estimate the adjusted hazard ratios (HR) for overall survival and HNC-specific survival, by cancer site. The study included 10,177 patients from 10 studies worldwide. Underweight patients had lower overall survival (HR = 1.69, 95%CI: 1.31–2.19) respect to those having normal weight with consistent results across the HNC sites. Overweight and obese patients with oropharyngeal cancers had a favourable HNC-specific survival (HR = 0.50 (95%CI: 0.33–0.75) and HR = 0.51 (95%CI: 0.36–0.72), respectively). Among ever smokers overweight and obese patients showed a favourable HNC-specific survival (HR = 0.69 (95%CI: 0.56–0.86) and HR = 0.70 (95%CI: 0.61–0.80)). Our findings show that high BMI values at cancer diagnosis predict the survival rates in smoking patients with HNC. This association may be explained by residual confounding, reverse causation, and collider stratification bias, but may also suggest that a nutritional reserve may help patients survive HNC cancer.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".