Effects of Funding Policy Changes and Health Warnings on the Use of Erythropoiesis-Stimulating Agents
Bibliographic record
Abstract
PURPOSE: To characterize the effects of formulary changes and governmental safety warnings on use of erythropoiesis-stimulating agents (ESAs) in patients with cancer. PATIENTS AND METHODS: We conducted a cross-sectional time-series analysis using health administrative data from Ontario, Canada. From January 1997 to December 2009 we identified all ESA initiations among patients diagnosed with cancer. We explored the effects of two formulary changes that progressively liberalized coverage for ESAs, first by rescinding the requirement for blood transfusion in 2003 and then by removing all restrictions in 2007. We also explored the effect of US Food and Drug Administration and Health Canada warnings issued in the second quarter of 2007. To assess regional variability in ESA use, we determined prescription rates for each of Ontario's 14 regional cancer centers. RESULTS: After the first formulary change, the ESA initiation rate increased to 1.66 new users per 1,000 patients with cancer, 374% more than predicted (P < .001). After the second formulary change, the initiation rate increased to 3.97 new users per 1,000 patients with cancer, 73% more than predicted (P < .001). After the safety warnings, this rate declined 81% by study end (P < .001). We found significant regional variation in ESA use. CONCLUSION: Formulary access and safety warnings had significant impacts on the new use of ESA drugs in patients with cancer. This suggests that both are effective means of influencing the use of these drugs. Variable ESA prescription rates across our region may reflect a lack of consensus regarding their utility.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".