Role of human papillomavirus in the clinical and histopathologic features of laryngeal and hypopharyngeal cancers.
Bibliographic record
Abstract
OBJECTIVE: In this study, we aimed to evaluate the role of human papillomavirus (HPV) in the clinical and histopathologic features of laryngeal and hypopharyngeal cancers. METHOD: Paraffin wax-embedded sections consisting of 89 laryngeal and hypopharyngeal cancers from 210 patients were analyzed. Tissue samples were amplified by using a glucose 6-phosphatase dehydrogenase (G6PDH) control kit (Eurogentec, Seraing, Belgium), and G6PDH-positive samples were thought to have appropriate tissues by using a deoxyribonucleic acid (DNA) extraction kit (DNA mini kit, Qiagen, Germany). HPV and HPV-16 were detected by real-time polymerase chain reaction (PCR) using the L1 region. Real-time nested amplifications of MY09/11 products were done by GP5+/GP6+ primers and a cyanine-5-labeled HPV-16 DNA-specific probe. HPV-16-negative MY09/11 amplicons were sequenced by an OpenGene automated DNA sequencing system, and a similar percentage of sequences was calculated by GeneObjects software (Visible Genetics, Canada). RESULTS: Specimens from 89 subjects fitting the criteria were taken for PCR assay, and the HPV genome was analyzed in 65 cases because the remaining cases did not have enough tissue according to G6PDH amplification. HPV was positive in 27 cases (41.5%). HPV positivity was found to be associated with lymph node metastasis (LNM). Odds ratio analysis indicated that HPV positivity was an important factor for LNM but not for other parameters. CONCLUSIONS: HPV-16 infection can be associated with laryngeal carcinomas without LNM. Analysis of HPV positivity could be used as a prognostic factor.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".