Bibliographic record
Abstract
Modern medicines improve both health outcomes and quality of life for those stricken with illness, and their ability to do so continues to improve and advance over time. Every day, researchers and scientists work to come up with new and innovative ways to treat illnesses, mitigate suffering, and prolong life while research-based pharmaceutical companies invest in the development and testing necessary to bring these innovations to market. The medicines that are available today are not only able to treat illnesses that could not previously be treated, but also represent a substitution for older, less efficient, and less effective methods of treatment. Even in cases where medicines may not have a different impact therapeutically, they can expand access to better health through reductions in adverse events and reactions, and may work better for some parts of the population poorly served by previous advances. However, access to these newer (and superior) pharmaceuticals is not equal across developed countries. This is, in part, the result of governmental regulations and approvals. Critically, new medicines are only accessible by the public after they have been granted regulatory clearance by the host jurisdiction’s responsible body such as Health Canada , the United States Food and Drug Administration (FDA), and the European Medicines Agency (EMA). The efficiency with which these agencies approve drugs and the numbers of drugs ultimately approved varies considerably between these regulatory authorities. While the potential for harm that accompanies any new medicine on the market may provide some justification for regulatory approval in general, the question of why such approval is duplicated in one jurisdiction (e.g., Canada) while it is being undertaken in another with comparable standards (e.g., Europe) remains. Indeed, to the extent submissions to these agencies and their efficiency in approving them vary, such duplication of effort reinforces the unfortunate reality that drugs are available to patients in different countries, at different points in time. This study aims to measure the difference in access to new medicines that results from duplication of effort in Canada. By compiling a list of new drugs approved in Canada between 2005-2011/12 (Health Canada moved from calendar-year to fiscal-year reporting in 2011/12), and comparing the corresponding approval dates with those in the United States and the European Union, we seek to provide Canadians an estimate of how much sooner these new drugs would have been available to them in the absence of what might be considered an unnecessary regulatory hurdle imposed by Health Canada.
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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 | 0.203 | 0.383 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.011 | 0.038 |
| Scholarly communication | 0.026 | 0.047 |
| Open science | 0.008 | 0.025 |
| Research integrity | 0.066 | 0.074 |
| Insufficient payload (model declined to judge) | 0.031 | 0.008 |
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".