Transparency too little, too late? Why and how Health Canada should make clinical data and regulatory decision-making open to scrutiny in the face of COVID-19
Bibliographic record
Abstract
Hard-won gains in the transparency of therapeutic product data in recent years1 have occurred alongside growing reliance by regulators upon expedited review processes.2 The concurrence of these two trends raises fundamental questions for the future of pharmaceutical regulation about whether the institutionalization of transparency will foster improved oversight of drugs, biologics, vaccines, and other interventions, or else, provide cover for a relaxing of regulatory standards of safety, effectiveness, and quality.3 The urgency of the COVID-19 pandemic, however, has brought this tension into immediate and sharp relief. During the course of the global health crisis, regulatory bodies have markedly expanded the number and use of expedited review processes for COVID-19 therapies, and at the same time, the proliferation of misinformation about any potential SARS-CoV-2 intervention4 reveals the limitations of recently implemented transparency measures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".