National treatment trends in human papillomavirus–positive oropharyngeal squamous cell carcinoma
Bibliographic record
Abstract
BACKGROUND: Human papillomavirus (HPV)-mediated oropharyngeal cancer (OPC) is associated with dramatically improved survival in comparison with HPV-negative OPC and can be successfully treated with surgical and nonsurgical approaches. National treatment trends for OPC were investigated with the National Cancer Data Base (NCDB). METHODS: The NCDB was reviewed for primary HPV-mediated OPC in 2010-2014. Multivariable regression was used to identify predictors of both nonsurgical therapy and receipt of adjuvant chemoradiation (CRT). RESULTS: = 0.89). Hospitals in the top treatment volume quartile (quartile 1 [Q1]; n = 29) had a lower rate of positive margins (16.3%) than bottom-quartile centers (n = 741; rate of positive margins, 36.4%; P < .001); Q1 hospitals used surgical therapy significantly more. Independent predictors of nonsurgical therapy included older age, advanced disease, lower hospital volume, and living closer to the hospital or outside the Pacific United States. In surgically treated patients, younger age, lower hospital volume, nodal disease, positive surgical margins, and extranodal extension (ENE) also predicted more adjuvant CRT use. CONCLUSIONS: The use of upfront surgical treatment decreased from 2010 to 2014. Hospital volume shows a strong, inverse correlation with the rate of positive surgical margins. The upfront treatment strategy is predicted not only by staging but also by patient-, geographic-, and hospital-specific factors. Lower hospital volume remains independently associated with increased triple-modality therapy after adjustments for positive margins, ENE, and pathologic staging.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.002 | 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".