Bibliographic record
Abstract
BACKGROUND: In Ontario, Canada, the 70/90 regulations were instituted in May 1993 to establish provincial government procurement prices for generic drugs. Accordingly, the first generic entrant's price could not exceed 70% of the incumbent's branded price. Subsequent entrants' prices could not exceed 90% of the first entrant's price. OBJECTIVE: These regulations' impact on generic market competitiveness are evaluated. DESIGN AND METHODS: Data on 518 drugs spanning nine therapeutic classifications were collected for the period of 04/01/1987 to 12/31/1998 from Ontario Drug Benefit formulary and IMS Canada. The period 04/01/1987 to 04/30/1993 was defined as the before period (BP) and 05/01/1993 to 12/31/1998 was the after period (AP). We compared the price ratio (P = P(G)/P(B) ) between BP and AP and performed regression analysis to assess the determinants of P. RESULTS: in both the BP and AP decreased as the number of generic firms increased within these periods. However, this decrease in was significantly less in the AP (median: 0.75 --> 0.68 --> 0.67) than in BP (0.71 --> 0.61 --> 0.53) as the number of generics increased from 1 to 2 to 3, respectively. The regression analysis showed that the price ratio in the AP was higher than that in the BP by 0.05, 0.09, and 0.13 for first, second, and third generic entrant respectively. CONCLUSIONS: Our findings show that the 70/90 regulations not only failed to achieve their goal of lowering the procurement price but instead the opposite occurred. The mandated procurement price became a focal point and resulted in a clustering of prices around the maximum allowable levels with little price dispersion.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".