Gender disparities on overall survival rates in HPV-associated head and neck cancer: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: The incidence of HPV-associated head and neck cancers, especially in the oropharyngeal region, is rising sharply, raising substantial clinical and public health concerns. These cancers are distinct from those caused by other etiologies such as tobacco and alcohol due to the unique prognosis of HPV-positive cases. Despite their generally better prognosis, there is significant uncertainty regarding the variation in survival outcomes between genders. This study aims to closely examine and understand gender differences in survival rates among patients with HPV-associated head and neck cancers, exploring potential disparities to inform treatment strategies and improve patient outcomes. Methods: A systematic review and meta-analysis were conducted using data from 13 studies involving 203,346 HNC patients. The studies were sourced from PubMed, Embase, and Web of Science, covering research until May 2024. The analysis involved calculating pooled hazard ratios (HRs) for survival, assessing heterogeneity and publication bias using the I² statistic, funnel plots, and Egger’s test. Results: The findings showed a slight, non-significant survival advantage for females in HPV-positive HNCs (HR 0.952). In HPV-negative HNCs, there was also no significant gender difference in survival (HR 1.053). The study noted high heterogeneity and significant publication bias. Conclusions: No significant gender disparities in survival for HPV-positive or HPV-negative HNCs, suggesting the need for personalized care strategies beyond gender considerations.
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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 | 0.011 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.033 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".