Selegiline shortage
Bibliographic record
Abstract
BACKGROUND: In September 2007, shortages of generic selegiline occurred, forcing patients to either switch to more expensive alternatives or forego treatment. We sought to evaluate prescription trends of generic selegiline and to quantify the economic impact of any resulting drug substitution of more expensive alternatives. METHODS: We analyzed proprietary data from IMS Health on monthly prescriptions in the United States for selegiline and potential substitutes from February 2002 through December 2007. Linear regression was used to predict the number of expected prescriptions after August 2007 had a shortage not occurred. The main outcome measures were the changes in prescriptions filled and the economic impact of drug substitution. RESULTS: Prior to the shortage, total prescriptions filled for generic selegiline decreased 42%, and supply consolidated into one company, Apotex Inc., Toronto, Canada, whose market share increased from 41% to 83%. During the first 4 months of the shortage, Apotex Inc. filled 10,500 fewer prescriptions than projected and other selegiline manufacturers filled 7,400 more than projected for a net shortage of 3,100 prescriptions. The number of branded selegiline capsules filled during this period increased by 1,800 above projections, and 1,300 prescriptions for generic selegiline were not refilled or substituted. The societal cost of substituting generic selegiline with branded capsules was $75,000 over the first 4 months of the shortage. CONCLUSIONS: Generic drug shortages carry economic and health implications. Given ongoing consolidation in the generics drug industry, these shortages may become more common and may require heightened regulatory scrutiny of the generic drug industry.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".