Treatment de‐escalation for <scp>HPV</scp>+ oropharyngeal cancer: A systematic review and meta‐analysis
Bibliographic record
Abstract
Human Papillomavirus (HPV) related oropharyngeal carcinoma (OPC) carries a better prognosis compared with HPV-counterparts, thereby pushing the adoption of de-intensification treatment approaches as new strategies to preserve superior oncologic outcomes while minimizing toxicity. We evaluated the effect of treatment de-intensification in terms of overall survival (OS), progression-free survival (PFS), locoregional and distant control (LRC and DM) by selecting prospective or retrospective studies, providing outcome data with reduced intensification versus standard curative treatment in HPV+ OPC patients, with a systematic analysis till September 2020. The primary outcome of interest was OS. Secondary endpoints were PFS, LRC, and DM expressed as HR. A total of 55 studies (from 1393 screened references) were employed for quantitative synthesis for 38 929 patients. Among n = 48 studies with data available, de-intensified treatments reduced OS in HPV+ OPCs (HR = 1.33, 95% CI 1.17-1.52; p < 0.01). In de-escalated treatments, PFS was also decreased (HR = 2.11, 95% CI 1.65-2.69; p < 0.01). Compared with standard treatments, reduced intensity approaches were associated with reduced locoregional and distant disease control (HR = 2.51, 95% CI 1.75-3.59; p < 0.01; and HR = 1.9, 95% CI 1.25-2.9; p < 0.01). Chemoradiation improved survival in a definitive curative setting compared with radiotherapy alone (HR = 1.42, 95% CI 1.16-1.75; p < 0.01). When adjuvant treatments were compared, standard and de-escalation strategies provided similar OS. In conclusion, in patients with HPV+ OPC, de-escalation treatments should not be widely and agnostically adopted in clinical practice, as therein lies a concrete risk of offering a sub-optimal treatment to 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".