Prescribing trends in direct-acting antivirals for the treatment of hepatitis C in Ontario, Canada
Bibliographic record
Abstract
Background: Direct-acting antivirals (DAA) offer an opportunity to cure hepatitis C. Reimbursement for DAAs has changed on two occasions since their inclusion on the Ontario public formulary. Whether these changes have appreciably modified prescribing patterns and increased access to DAAs is unknown. Methods: We conducted a repeated cross-sectional study of DAA reimbursement by the Ontario Public Drug Programs from January 1, 2012, to December 31, 2018, to summarize the use of DAAs in Ontario and describe changes in DAA prescribing physician specialties over this period. We measured the total number of users quarterly. Results are reported overall and by prescriber type. Results: = 17,813; 65.7%) of all DAAs were prescribed by gastroenterologists, hepatologists, or infectious disease specialists. Use of DAAs over time appears to have had three major phases in uptake: (1) the introduction of DAA treatments on the Ontario public drug formulary as a prior authorization benefit in Q1 2015, (2) expanded listing of all DAAs as limited-use products on the formulary in Q1 2017, and (3) the introduction of newer DAAs in Q2 2018. Conclusions: Changes in listing of these agents had a direct impact on the use of DAAs overall. Generally, broader listing expanded access but did not appear to shift utilization patterns to primary care prescribers. Further understanding of who is not receiving treatment is needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".