Bibliographic record
Abstract
Canada’s Drug Agency recommends that Itovebi in combination with palbociclib (PAL) and fulvestrant (FUL) be reimbursed by public drug plans for the treatment of adult patients with endocrine-resistant, PIK3CA-mutated, hormone receptor–positive, HER2-negative, locally advanced or metastatic breast cancer, following recurrence on or after completing adjuvant endocrine treatment, only if certain conditions are met. Itovebi in combination with PAL and FUL should only be covered to treat adults with hormone receptor–positive, HER2-negative breast cancer that has spread to nearby tissue or lymph nodes (locally advanced), or to other parts of the body (metastatic); has come back after hormone (endocrine) therapy; and has an abnormal PIK3CA Patients should also have good performance status. Itovebi in combination with PAL and FUL should not be covered if the patient has been previously treated for hormone receptor–positive, HER2-negative metastatic breast cancer with mutations in the PIK3CA gene, or if they have uncontrolled diabetes. Itovebi in combination with PAL and FUL should be prescribed by, then managed under the care of, health care professionals with expertise in managing advanced or metastatic breast cancer. Reimbursement of Itovebi should be discontinued if the cancer becomes worse or there are unacceptable side effects. Price reductions exceeding 90% in the cost of Itovebi as part of the combination regimen with CDK4/6 inhibitor and FUL would be required to achieve an incremental cost-effectiveness ratio below $50,000 per quality-adjusted life-year gained, relative to therapies currently in use.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.073 | 0.029 |
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".