Bibliographic record
Abstract
Canada’s Drug Agency (CDA-AMC) recommends that Enhertu be reimbursed by public drug plans for the second-line treatment of adult patients with unresectable, locally advanced, or metastatic HER2-positive gastric or gastroesophageal junction (GEJ) adenocarcinoma who have received a prior anti–HER2-based regimen for a time-limited period while additional evidence is generated if certain conditions are met. Please note that time-limited reimbursement refers to temporary reimbursement by the drug programs while additional evidence is generated and submitted for reassessment (i.e., this does not refer to the length of treatment or number of cycles administered). Enhertu should only be covered to treat adult patients with unresectable, locally advanced, or metastatic HER2-positive gastric or GEJ adenocarcinoma, have previously received trastuzumab-based treatment for locally advanced or metastatic disease, are in relatively good health, and do not have symptomatic spinal cord compression, clinically active central nervous system metastases, or current interstitial lung disease or pneumonitis. Enhertu should only be reimbursed if it is prescribed by clinicians with experience and expertise in treating gastric or GEJ adenocarcinoma and the cost of Enhertu is reduced.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".