Real-World Evidence on the Effectiveness of Pembrolizumab in Patients With Recurrent/Metastatic/Unresectable Head and Neck Squamous Cell Cancer: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The aim of the review was to systematically review real-world data on the effectiveness and safety of pembrolizumab in recurrent/metastatic/unresectable head and neck squamous cell cancer (HNSCC) patients. Two independent reviewers retrieved the studies separately and simultaneously. PubMed, Embase, Scopus, Web of Science, and Cochrane Central were searched for prospective and retrospective studies on recurrent/metastatic/unresectable HNSCC patients treated with either pembrolizumab monotherapy or pembrolizumab combination therapy published till November 2024. Studies published in the English language and available as full-text articles were included while studies with patients treated with additional chemotherapeutic agents that do not include chemotherapy with fluorouracil, platinum, cetuximab, or radiotherapy were excluded. Outcomes were overall response rate, progression-free survival, overall survival, and adverse events. Forest plots were generated using RevMan software version 5.4 (2020; The Cochrane Collaboration, London, United Kingdom). The ROBINS-I (Risk Of Bias In Non-randomised Studies - of Interventions) tool was used for the assessment of the potential risk of bias and the quality of evidence was synthesized using GRADEpro (Evidence Prime Inc., Hamilton, Canada). We identified 5884 records, removed 2784 duplicate records, and screened 3100 studies. A total of 2931 records were excluded based on title/abstract. Of the remaining 169 articles, nine studies satisfied eligibility criteria and were included for final review. Pooled data suggested that 1006 patients were administered with pembrolizumab monotherapy while 448 patients received pembrolizumab combination therapy. Monotherapy improved the overall response rate compared to combination therapy. No significant difference in progression-free survival and overall survival was found between the two groups. Adverse events were reported less in pembrolizumab monotherapy compared to pembrolizumab combination therapy. The studies were assessed at high risk of bias and graded at low to very low quality of evidence. The study showed some beneficial effects of pembrolizumab monotherapy in recurrent/metastatic/unresectable HNSCC patients in real-world scenarios. However, more studies are required to generate robust evidence.
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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 | 0.023 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.027 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".