Risk factors and survival impact of severe radiation-related late toxicities in head and neck cancer–a cohort study
Bibliographic record
Abstract
Background: Radiation late toxicities (RLTs) are complications of curative-intent radiotherapy (RT) for head and neck cancer (HNC) and are increasingly relevant due to younger age at diagnosis and improved survival outcomes. Methods: We conducted a cohort study of HNC patients who received ≥50 Gy as part of curative treatment between January 2003 and December 2020 at a Canadian quaternary cancer center. Risk factors for severe RLTs (≥RTOG Grade 3) were evaluated using time-to-event analyses. Actuarial rates of RLT and overall survival (OS) were estimated using competing risk and Kaplan-Meier methods, respectively. Cox proportional hazard models identified factors associated with RLT and OS. Findings: subgroup (n = 4650) with ≥2 years of follow-up and no recurrence was also identified. Modifiable risk factors for RLTs included RT technique, dose, neck irradiation, neck dissection, smoking status, and chemotherapy (p ≤ 0.012). Non-modifiable factors included younger age, female sex, and oral cavity primaries (p ≤ 0.012). In multivariable analysis, RLTs were associated with increased mortality (HR = 2.1, 95% CI: 1.8-2.5, p < 0.001), but RLT's impact on OS was lessened among patients referred to the Adult Radiation Late Effects Clinic (ARLEC) (HR = 1.7, 95% CI: 1.3-2.4). Interpretation: RLTs are common and associated with worse survival among HNC survivors. Identification of modifiable risk factors provides opportunities for prevention. Multidisciplinary management of RLTs in specialized clinics may help improve the outcomes in this growing survivorship population. Funding: No external funding was utilized for this study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".