Bibliographic record
Abstract
Canada’s Drug Agency (CDA-AMC) recommends that Casgevy be reimbursed by public drug plans for the treatment of transfusion-dependent beta-thalassemia (TDT) if certain conditions are met. Casgevy should only be covered to treat patients 12 years of age and older with a diagnosis of TDT (defined as documented homozygous or compound heterozygous beta-thalassemia, and having received transfusions of packed red blood cells [RBCs] of at least 100mL/kg/year, or 10 units/year, during the previous 2 years), who meet specific Karnofsky (patients 16 years or older) or Lansky (patients under 16 years of age) performance status thresholds, who are eligible for autologous stem cell transplant, do not have an available and willing 10/10 HLA-matched related donor, have not previously received allogenic hematopoietic stem cell transplant (allo-HSCT) or prior gene editing therapy or gene editing products, and who do not have any of the following: associated alpha thalassemia and 1 or more alpha deletions or alpha multiplications, prior or current history of malignancy, or sickle cell beta-thalassemia variant. Casgevy should only be reimbursed if prescribed by a hematologist with expertise in TDT, if it is not a re-treatment (Casgevy is a 1-time treatment), and the cost of Casgevy 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.102 | 0.034 |
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".