Are we ready for deintensification in human papillomavirus-positive oropharyngeal carcinomas?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Excellent outcomes following contemporary treatment of human papillomavirus (HPV)-positive oropharyngeal carcinoma (HPV+ OPC) have prompted the exploration of deintensification approaches to minimize treatment-related toxicities. This review describes the landscape of deintensification to date (up to November 2022). RECENT FINDINGS: Although several deintensification trials have been published, none are practice changing. Three phase III randomized-controlled trials studying cetuximab and radiation therapy vs. standard chemoradiotherapy all showed inferior outcomes. Although some phase II trials reported favourable outcomes, they are often single-arm trials without an adequate control arm, thereby limiting the ability to modify practice. SUMMARY: Substantial effort has been expended to explore deintensification options for selected HPV+ OPC patients aiming to avoid unnecessary toxicity. Strategies have included replacing cisplatin with cetuximab, reduced chemotherapy or radiotherapy intensity, reduction of radiotherapy volumes and risk stratification after trans-oral surgery or following induction chemotherapy. Challenges remain in the current deintensification landscape, including identifying the most suitable candidates along with a choice of most appropriate deintensification strategies. Promising selection criteria included either static baseline features or kinetic characteristics of clinical-biological parameters. Practice-changing trials remain elusive, and the search continues to attempt optimization of the therapeutic ratio for these patients.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".