Human papillomavirus and p16 immunostaining, prevalence and prognosis of squamous carcinoma of unknown primary in the head and neck region
Bibliographic record
Abstract
The prevalence of human papillomavirus (HPV) in squamous cell carcinoma of unknown primary in the head and neck (SCCUPHN), and prognosis by HPV status of SCCUPHN patients has been difficult to estimate because of the rarity of this subtype. In MEDLINE, Epub Ahead of Print, In-Process & Other Non-Indexed Citations, EMBASE, Cochrane library and Web of Science searches, observational studies and clinical trials that reported survival rates of patients with SCCUPHN by HPV status were identified. Meta-analysis estimated the prevalence and prognosis (overall survival, OS; progression-free survival, PFS) of SCCUPHN by HPV status, and compared them to studies of oropharyngeal squamous cell carcinoma (OPSCC) from the same institutions and across continents. In 17 SCCUPHN studies (n = 1,149) and 17 institution-matched OPSCC studies (n = 6,522), the pooled HPV prevalence of SCCUPHN was 49%, which was only 10% (95%CI: 1-19%) lower than OPSCC prevalence in the underlying population. Estimated 5-year OS for HPV-negative SCCUPHN was 44% (95%CI: 36-51%) vs. HPV-positive SCCUPHN of 91% (95%CI: 86-96%); hazard ratio (HR) for OS was 3.25 (95%CI: 2.45-4.31) and PFS was 4.49 (95%CI: 2.88-7.02). HRs by HPV status for OPSCC were similar to that in SCCUPHN. While North American SCCUPHNs had higher HPV prevalence than European SCCUPHNs (OR = 2.68 (95%CI: 1.3-5.6)), HR of OS for HPV-negative vs. HPV-positive patients were similar in both continents (HRs of 3.78-4.09). Prevalence of HPV among SCCUPHN patients were lower than in OPSCC. The survival benefit conferred by being HPV-positive was similar in SCCUPHN as in OPSCCs, independent of continent.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 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".