Consensus Statement Regarding the Application of Biogen to Health Canada for Approval of Aducanumab
Bibliographic record
Abstract
Alzheimer's disease is a major cause of morbidity and mortality. Currently, there are no disease-modifying pharmacotherapies for this condition. Aducanumab, an amyloid beta-directed monoclonal antibody that targets aggregated forms of amyloid-beta in the brains of people with Alzheimer's disease, has raised hopes that such a therapy has been discovered, but its approval by the US Food and Drug Administration has engendered a good deal of controversy. A similar application for approval has been submitted to Health Canada. In response to this, a group of Canadian clinical dementia experts representing a number of organizations, including the Canadian Geriatrics Society, was convened by the Canadian Consortium on Neurodegeneration in Aging (CCNA) to discuss the evidence currently available on this agent and seek consensus on what advice they would offer Health Canada on the application. There was wide-spread agreement that it would be premature for aducanumab to receive approval for the treatment of Alzheimer's disease. It was also noted that the Canadian health-care system is poorly prepared at this time to deal with a disease-modifying therapeutic with targeting, administration, and monitoring characteristics like aducanumab. In this paper, the consensus reached is presented along with its underlying rationale.
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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 | 0.066 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.066 | 0.042 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".