Concurrent immunotherapy and radiation in cisplatin-ineligible patients with HNSCC: a systematic review & meta-analysis
Bibliographic record
Abstract
Background Head and squamous cell carcinoma (HNSCC) in the locally advanced setting is challenging to treat and remains an area of significant morbidity and mortality. For patients who are cisplatin-ineligible and considered unresectable, there is no clear standard of care including the choice of radiosensitizer.Methods OVID Medline, EMBASE, and the Cochrane Central Register of Controlled Trials were systematically searched. Randomized clinical trials (RCTs) of cisplatin-ineligible unresectable HNSCC patients who were randomized to an immune checkpoint inhibitor concurrent with radiation compared to a control arm were included. Data was pooled and assessed using a random-effects model for the meta-analysis.Results Three eligible RCTs were identified and analyzed which comprised 594 patients whose median age ranged from 65–72 years. There was no difference in overall survival (pooled HR 1.02, 95% CI 0.76–1.37, p = 0.88) or PFS (pooled HR 0.92, 95% CI 0.68–1.23, p = 0.56). Grade greater than or equal to 3 adverse events were favored to be less in the immunotherapy arm.Conclusions and Relevance Immune-checkpoint inhibitors are not superior to cetuximab when used with definitive radiation in HNSCC. Further study is warranted, given its potential signal for non-inferior survival with less toxicity profile trends.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.030 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".