Influencing antiemetic prescribing practices and funding changes through evidence-based guidelines.
Bibliographic record
Abstract
31 Background: In 2013 Cancer Care Ontario released updated antiemetic recommendations supporting the use of aprepitant-based combinations as 1st line therapy for highly emetogenic and 2nd line therapy for moderately emetogenic chemotherapy and discouraging the prolonged use of 5-HT3 antagonists. In 2014 changes were made in the Ontario drug formulary to align public funding to those recommendations. The impact of the changes in guidance and public funding on prescribing practices are now being analyzed. Methods: Using the Ontario Drug Benefit (ODB) database, data was extracted to analyze the prescribing practices of aprepitant, granisetron and ondansetron for chemotherapy-induced emesis between the pre-funding period (November 2013 to September 2014) and post-funding period (October 2014 to July 2015). Results: Prior to funding changes, an average of 197 prescriptions/month of aprepitant were billed to the ODB program totaling $22,422. After funding, an average of 1,165 prescriptions/month of aprepitant were billed totaling $132,145. This represented a 490% increase in utilization. The combined 5-HT3 receptor antagonists prescriptions/month billed during the respective time periods were 5,592 ($405,604) and 5,536 ($402,628). This represented a 1% decrease in utilization. Conclusions: There was a significant increase in aprepitant utilization and total expenditure to the ODB program indicating strong uptake of the triple-drug recommendation for highly emetogenic regimens. However, there was minimal change in prescribing practices related to the 5-HT3 receptor antagonists, indicating a reluctance to decrease utilization. Further work is necessary to discourage the prolonged use of 5-HT3 receptor antagonists.
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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 | 0.086 | 0.313 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".