The Prognostic Impact of Radiotherapy Delays in Oropharynx Carcinoma and the Role of p16 Status
Bibliographic record
Abstract
OBJECTIVE: A retrospective analysis was performed to evaluate the prognostic significance of treatment delays (TDs) for oropharynx carcinoma patients treated with definitive radiotherapy (RT), comparing p16+ versus p16- disease. MATERIALS AND METHODS: Patients treated between 2012 and 2016 were analyzed (n=763). TD was defined as the time from pathologic diagnosis to initiation of RT. TD thresholds of ≤60, 61 to 90, and >90 days were used to stratify outcomes. Time on treatment (TOT) delays were estimated based on the RT fractionation. TOT delay of 1 to 3 days was compared with >3 days. Predictors of cancer-specific survival (CSS) and locoregional recurrence (LRR) were evaluated on multivariable analysis. RESULTS: Six hundred fifty (85%) patients had p16+ disease. On multivariable analysis, TOT delay of 1 to 3 days versus <1 day was associated with inferior CSS (hazard ratio [HR]=1.81; 95% confidence interval [CI]: 1.02-3.22). TD >90 versus ≤60 days (HR=1.68; 95% CI: 0.98-3.04) and 61 to 90 versus ≤60 days (HR=0.94; 95% CI: 0.60-1.48) was not associated with CSS. TD >90 versus ≤60 days (HR=1.29; 95% CI: 0.66-2.52), TD 61 to 90 versus ≤60 days (HR=0.98; 95% CI: 0.64-1.52), TOT 1 to 3 versus <1 day (HR=0.91; 95% CI: 0.39-2.11), and TOT >3 versus <1 day (HR=1.79; 95% CI: 0.80-3.99) were not associated with LRR. There was no interaction between p16 status and TD in relation to LRR (P=0.27) or CSS (P=0.17). CONCLUSIONS: TDs were not significantly associated with CSS or LRR. TOT of 1 to 3 days was associated with inferior CSS. p16 status should not be a significant factor when triaging RT start dates.
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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 | 0.000 | 0.001 |
| 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".