Nivolumab for Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma: A Retrospective Tertiary Centre’s Real-World Experience
Bibliographic record
Abstract
Nivolumab, a PD-1 checkpoint inhibitor, was approved in Canada in 2017 for the treatment of recurrent or metastatic head and neck squamous cell carcinoma (R/M HNSCC) based on the phase 3 trial CHECKMATE-141. We aimed to examine the demographics and efficacy of nivolumab in a Canadian, real-world setting. A retrospective chart review was performed on patients who received nivolumab for R/M HNSCC from 2017 to 2020 at a high-volume cancer centre. Data were abstracted from 34 patients, based on physician notes and imaging reports. The median patient age at nivolumab initiation was 61, 24% were female, and 62% were current or former smokers. Prior to nivolumab, 44% of patients underwent surgery, 97% radiation, and 100% chemotherapy. Most (97%) therapies were for primary disease. Overall survival at 6 and 12 months following drug initiation was 38% and 23%, respectively. Progression-free survival at 6 and 12 months was 33% and 22%, respectively. Eighteen percent of patients experienced an immune-related adverse event, the most common of which was pneumonitis (3/8) and endocrine events (3/8). Seven out of eight of the immune adverse events were grade 1-2; 1/8 was grade 3. Nivolumab appears to have decreased survival rates in our single-centre Canadian population compared to CHECKMATE-141 and presented a manageable adverse event profile for 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".