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.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.025 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".