Bibliographic record
Abstract
Canada’s Drug Agency (CDA-AMC) recommends that Rybrevant should be reimbursed by public drug plans when used in combination with carboplatin and pemetrexed for the first-line treatment of adult patients with locally advanced (not amenable to curative therapy) or metastatic non–small cell lung cancer (NSCLC) with activating EGFR exon 20 insertion (ex20ins) mutations if certain conditions are met. Rybrevant in combination with carboplatin and pemetrexed should only be covered to treat patients who have NSCLC with a specific EGFR gene mutation called ex20ins, have a good performance status, and the cancer has spread to other parts of the body or cannot be removed by surgery. Rybrevant in combination with carboplatin and pemetrexed should only be reimbursed when started in combination with platinum-based chemotherapy (i.e., carboplatin and pemetrexed), and the cost of Rybrevant is reduced. It should not be reimbursed for patients with untreated brain metastases or those who have had previous systemic therapy, adjuvant treatment (given after surgery), or neoadjuvant treatment (given before surgery) if those treatments were completed less than 6 months before the cancer worsened. Rybrevant in combination with carboplatin and pemetrexed must be prescribed by specialists with experience managing NSCLC.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".