The impact of national holidays on postoperative radiotherapy of squamous cell carcinoma of the head and neck
Bibliographic record
Abstract
Background: Delays in starting postoperative radiotherapy (PORT) have been established as negative predictors for clinical outcomes in head and neck squamous cell carcinomas (HNSCC). Our study aimed to examine the effect of delays during PORT, and the impact of national holidays in Canada, a publicly funded system, on oncologic outcomes such as Overall Survival (OS) and Local Recurrence (LR). Methods: The provincial cancer registry was queried to obtain demographic, pathologic, and outcomes data from cancer patients treated for all squamous cell carcinomas of the head and neck region treated between January 1, 2007 and November 30, 2019. All extracted information was cross-referenced and supplemented by chart review of patient electronic medical records. Extracted data were analyzed for OS and LR, in the context of Canadian national holidays causing delays during PORT. Results: 1433 patients treated for HNSCCs were identified, of whom 338 were treated curatively with surgery followed by PORT. 68.6% of patients experienced at least one day of interruption during treatments due to holidays. LR was 15.4% and OS was 59.6% at 5 years. Treatment interruptions by holidays were predictive of local recurrence (HR, 2.38; 95% CI 1.17-4.83; p = 0.017). Patients that developed early recurrence prior to PORT had very poor oncologic outcomes. Conclusion: Our findings were consistent with previously published studies in limiting the interval between surgery and PORT. We identified the novel finding of paired holidays as a significant predictor in determining LR, suggesting the importance of modifying RT delivery schedules and timing.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".