The Prognostic and Predictive Effects of Human Papillomavirus Status in Hypopharyngeal Carcinoma: Population-Based Study
Bibliographic record
Abstract
BACKGROUND: The role of the Human Papillomavirus (HPV) status in patients with hypopharyngeal squamous cell carcinoma (HSCC) remains controversial. OBJECTIVE: Our aim was to determine the prognostic and predictive effects of HPV status in patients with locally advanced HSCC (stage III-IVB) receiving primary radiotherapy. METHODS: Patients diagnosed with stage III-IVB HSCC between 2010 and 2016 were identified. HPV status, demographics, clinicopathological characteristics, treatment, and survival data were captured. Kaplan-Meier analysis, multivariable Cox regression analysis, and propensity score matching analysis were performed. RESULTS: We identified 531 patients in this study and 142 (26.7%) patients with HPV-positive diseases. No significant differences were observed between those with HPV-negative and HPV-positive diseases with regard to demographics, clinicopathological characteristics, and chemotherapy use. HPV-positive HSCC had better head and neck cancer-specific survival (HNCSS; P=.001) and overall survival (OS; P<.001) compared to those with HPV-negative tumors. Similar results were found using the multivariable Cox regression analysis. Sensitivity analyses showed that the receipt of chemotherapy was associated with significantly improving HNCSS (P<.001) and OS (P<.001) compared to not receiving chemotherapy in HPV-negative HSCC, whereas comparable HNCSS (P=.59) and OS (P=.12) were found between both treatment arms in HPV-positive HSCC. Similar results were found after propensity score matching. CONCLUSIONS: Approximately one-quarter of HSCC may be HPV-related, and HPV-positive HSCC is associated with improved survival outcomes. Furthermore, additional chemotherapy appears to be not related to a survival benefit in patients with HPV-positive tumors who received primary radiotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".