Eligibility and workload implications of adjuvant immunotherapy in patients with resected esophageal and gastroesophageal junction (ESO/GEJ) cancer
Bibliographic record
Abstract
Background The CheckMate (CM) 577 trial demonstrated the efficacy of 12 months of adjuvant nivolumab for esophageal (ESO)/gastroesophageal junction (GEJ) cancer patients with residual pathological disease after neoadjuvant chemoradiation. Nivolumab is now an approved and funded regimen in British Columbia (BC). This retrospective study examines its real-world eligibility and resource implications. Materials and methods We conducted an ethics board-approved chart review of patients who underwent CROSS chemoradiation at BC Cancer from January 2016 to December 2020. Patient eligibility was determined per CM577 and GIAJNIV criteria. We assessed the resource impact of nivolumab by projecting the number of MD and chemotherapy visits, and anticipated G3/4 toxicity events per the CM577 trial. Results We identified 677 patients (63% ESO and 37% GEJ; 74% adenocarcinomas and 25% squamous cell carcinomas). Among the 460 who had resection, 79% had residual pathological disease. 68% ( n = 249) and 88% ( n = 321) were eligible for adjuvant nivolumab per CM577 and GIAJNIV criteria, respectively. In BC, this equates to ∼60 patients/year, resulting in 768 additional chemotherapy and potentially an equal number of MD visits, totaling 256 MD workhours annually. With a 34% G3/4 toxicity rate, an estimated 20 patients/year may require medical intervention. Conclusions Adjuvant nivolumab is an important treatment option for resected ESO/GEJ cancer patients. Our findings suggest that a substantial portion (88%) of those with residual pathological disease would be eligible for 12 months of therapy. In addition to treatments costs, the implementation of new therapy indications should consider the additional workload impact on oncologists.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".