Bibliographic record
Abstract
Reimbursement reviews are comprehensive assessments of the clinical effectiveness and cost-effectiveness, as well as patient and clinician perspectives, of a drug or drug class. The assessments inform nonbinding recommendations that help guide the reimbursement decisions of Canada’s federal, provincial, and territorial governments, with the exception of Quebec. This review assesses elexacaftor-tezacaftor-ivacaftor plus ivacaftor (Trikafta) Aged 2 to 5 years elexacaftor 100 mg, tezacaftor 50 mg, and ivacaftor 75 mg (granules) plus ivacaftor 75 mg (granules) elexacaftor 80 mg, tezacaftor 40 mg, and ivacaftor 60 mg (granules) plus ivacaftor 59.5 mg (granules), oral Aged 6 years and older 50 mg elexacaftor, 25 mg tezacaftor, and 37.5 mg ivacaftor (combination tablet) plus 75 mg ivacaftor (tablet), oral 100 mg elexacaftor, 50 mg tezacaftor, and 75 mg ivacaftor (combination tablet) plus 150 mg ivacaftor (tablet), oral. Indication: For the treatment of cystic fibrosis in patients aged 2 years and older who have at least 1 F508del mutation in the CFTR gene or a mutation in the CFTR gene that is responsive based on clinical and/or in vitro data.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".