De-Escalation After DE-ESCALATE and RTOG 1016: A Head and Neck Cancer InterGroup Framework for Future De-Escalation Studies
Bibliographic record
Abstract
Human papillomavirus (HPV)-positive oropharyngeal cancer (OPC) is increasing rapidly. The younger age, significantly improved prognosis, and relative morbidity of the standard-of-care cisplatin and radiotherapy in this population have led to the popularization of the concept of treatment de-escalation. The recent results of the first 3 randomized de-escalation trials, however, have shown a clear detriment in survival when cisplatin is omitted or substituted. In view of these results, the Head and Neck Cancer International Group identified the need to issue guidance regarding future de-escalation studies for patients with HPV-positive head and neck cancer to avoid the possibility of patients being harmed. We review the current state of the literature regarding HPV de-escalation trials and present a framework and guidance on future and existing clinical trials for treatment de-escalation of HPV-positive OPC. De-escalation paradigms of HPV-positive OPC should be evaluated in phase II studies, and results should be awaited before proceeding to phase III studies. Implementation into clinical practice before high-level evidence is available should not be undertaken in this context. Finally, harm-minimization techniques should also be evaluated as an alternative to de-escalation of treatment in these patient groups.
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 | 0.434 | 0.264 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".