Network meta-analysis of pembrolizumab as monotherapy and in combination with chemotherapy for first-line treatment of recurrent or metastatic head and neck squamous cell carcinoma
Bibliographic record
Abstract
Pembrolizumab monotherapy or in combination with chemotherapy is approved as first-line treatment in recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) based on improved overall survival (OS) versus EXTREME regimen in the KEYNOTE-048 trial. The clinical outcomes of pembrolizumab were compared with other recommended first-line treatments in R/M HNSCC in this study through a Bayesian network meta-analysis. A systematic literature review was conducted in July 2022, from which six trials that matched the KEYNOTE-048 patient eligibility criteria were included in the network. The OS and progression-free survival (PFS) outcomes were compared in the approved pembrolizumab indication (i.e., total population for pembrolizumab in combination with chemotherapy and combined positive score [CPS] ≥ 1 population for pembrolizumab monotherapy). A significant OS improvement was observed for pembrolizumab in combination with chemotherapy and pembrolizumab monotherapy versus EXTREME regimen (hazard ratio, 95% credible interval: 0.72, 0.60-0.86; 0.73, 0.60-0.88), platinum+5- FU (0.58, 0.43-0.76; 0.58, 0.44-0.78), and platinum+paclitaxel (0.53, 0.35-0.79; 0.53, 0.35-0.81), respectively. A non-significant numeric trend in OS improvement was observed versus the TPEx regimen. PFS was comparable with most first-line treatments and was improved versus platinum+5-FU (0.48, 0.36-0.64; 0.59, 0.45-0.79). Additional analyses in higher CPS subgroups also showed consistent results. Overall, our study results showed an improvement in OS outcomes versus alternative first-line treatments, consistent with the findings of the KEYNOTE-048 trial. These data support using pembrolizumab as a suitable firstline treatment option in R/M HNSCC.
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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 | 0.018 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.040 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| 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".