Implementing Canada's Data Exclusivity Obligations and Protecting Personal Information in Clinical Trials
Bibliographic record
Abstract
This thesis explores, in the context of pharmaceutical clinical trials, Canadian federal, provincial and territorial personal data protection laws (which are consistent with Canada’s membership in the international Organization for Economic Cooperation and Development). This thesis establishes that, despite scholarly concerns over de-identifiability of data, these laws govern collection, use, dissemination, and disposal of data about individuals in clinical trials right through and including applications made by innovator pharmaceutical companies to the federal government for approval to market new drugs. At this latter point, federal data exclusivity regulations also apply (as required by international trade agreements). This thesis establishes that both personal data protection and data exclusivity apply to clinical trials only for defined periods. Finally this research demonstrates that, unlike protection of confidential information which remains secret and does not contribute to the public good of access to information, data exclusivity displays characteristics of classic intellectual property.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.177 | 0.282 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.038 |
| Scholarly communication | 0.027 | 0.008 |
| Open science | 0.005 | 0.010 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".