Canadian Consensus Recommendations for Predictive Biomarker Testing in Gastric and Gastroesophageal Junction Adenocarcinoma
Bibliographic record
Abstract
Gastric cancer is common globally and has a generally poor prognosis with a low 5-year survival rate. Targeted therapies and immunotherapies have improved the treatment landscape, providing more options for efficacious treatment. The use of these therapies requires predictive biomarker testing to identify patients who can benefit from their use. New therapies on the horizon, such as CLDN18.2 monoclonal antibody therapy, require laboratories to implement new biomarker tests. A multidisciplinary pan-Canadian expert working group was convened to develop guidance for pathologists and oncologists on the implementation of CLDN18.2 IHC testing for gastric and gastroesophageal junction (G/GEJ) adenocarcinoma in Canada, as well as general recommendations to optimize predictive biomarker testing in G/GEJ adenocarcinoma. The expert working group recommendations highlight the importance of reflex testing for HER2, MMR and/or MSI, CLDN18, and PD-L1 in all patients at first diagnosis of G/GEJ adenocarcinoma. Testing for NTRK fusions may also be included in reflex testing or requested by the treating clinician when third-line therapy is being considered. The expert working group also made recommendations for pre-analytic, analytic, and post-analytic considerations for predictive biomarker testing in G/GEJ adenocarcinoma. Implementation of these recommendations will provide medical oncologists with accurate, timely biomarker results to use for treatment decision-making.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".