Factors associated with the development of second primary tumours in head and neck cancer patients
Bibliographic record
Abstract
INTRODUCTION: The development of second primary tumours (SPTs) is one of the main causes of low survival in patients with head and neck cancer (HNC). The aim of this study was to review the evidence about factors associated with developing SPTs in patients with HNC. METHODS: An updated systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines, and the search was performed in Pubmed and Scopus. Only original articles with a cohort or case-control design were included. Article quality was assessed with the Newcastle-Ottawa scale. RESULTS: Thirty-six and two case-control studies were included, with quality medium (n = 5) to high (n = 33). Tobacco showed a significant association with SPT development, with risks ranging from 1.41 (95%CI: 1.04-1.91) to 5.52 (95%CI: 2.91-10.49). Regarding alcohol, risks ranged from 1.46 (95%CI: 1.12-1.91) to 21.3 (95%CI: 2.9-156). Location of the index tumour in the hypopharynx/oropharynx, absence of human papillomavirus and presence of a premalignant lesion also increased the risk of SPTs. More controversy was found for sex, age and other clinical factors of the tumour. CONCLUSION: Toxic lifestyle habits and clinical factors were associated with the risk of SPTs in HNC patients. These findings may improve individualised prevention strategies in its follow-up.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".