Nomogram to Predict the Benefit of Intensive Treatment for Locoregionally Advanced Head and Neck Cancer
Bibliographic record
Abstract
Abstract Purpose: Previous studies indicate that the benefit of therapy depends on patients' risk for cancer recurrence relative to noncancer mortality (ω ratio). We sought to test the hypothesis that patients with head and neck cancer (HNC) with a higher ω ratio selectively benefit from intensive therapy. Experimental Design: We analyzed 2,688 patients with stage III–IVB HNC undergoing primary radiotherapy (RT) with or without systemic therapy on three phase III trials (RTOG 9003, RTOG 0129, and RTOG 0522). We used generalized competing event regression to stratify patients according to ω ratio and compared the effectiveness of intensive therapy as a function of predicted ω ratio (i.e., ω score). Intensive therapy was defined as treatment on an experimental arm with altered fractionation and/or multiagent concurrent systemic therapy. A nomogram was developed to predict patients' ω score on the basis of tumor, demographic, and health factors. Analysis was by intention to treat. Results: Decreasing age, improved performance status, higher body mass index, node-positive status, P16-negative status, and oral cavity primary predicted a higher ω ratio. Patients with ω score ≥0.80 were more likely to benefit from intensive treatment [5-year overall survival (OS), 70.0% vs. 56.6%; HR of 0.73, 95% confidence interval (CI): 0.57–0.94; P = 0.016] than those with ω score <0.80 (5-year OS, 46.7% vs. 45.3%; HR of 1.02, 95% CI: 0.92-1.14; P = 0.69; P = 0.019 for interaction). In contrast, the effectiveness of intensive therapy did not depend on risk of progression. Conclusions: Patients with HNC with a higher ω score selectively benefit from intensive treatment. A nomogram was developed to help select patients for intensive therapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".