A Canadian algorithm for upper gastrointestinal cancer management
Bibliographic record
Abstract
Recent advances in immunotherapy have changed the treatment landscape for cancers of the upper gastrointestinal (GI) system. Immune checkpoint inhibitors can lead to better survival and improved quality of life for affected individuals. Adopting new treatment strategies in real-world practice can be challenging, and algorithms that are easy to implement in Canadian oncology practices would benefit clinicians and patients. In this study, we present expert opinion on best practices for upper GI cancer management, including a new algorithm that integrates the latest evidence for screening, workup, diagnosis, treatment, and survivorship. The algorithm is based on a novel approach comprising a case-based, accredited educational program with asynchronous discussion among clinicians practicing across Canada, with the input of expert medical oncologists and gastroenterologists. A needs assessment was employed to determine current areas of educational need in the field of upper GI cancers, and a patient representative provided insights into patient concerns and priorities. The best practices described here include seeking patient input throughout treatment, integrating immune checkpoint inhibitors into systemic therapy for both localized and advanced disease, and providing comprehensive supportive care throughout the treatment and survivorship journey.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".