Bibliographic record
Abstract
Another recurring theme was a desire for transparency throughout the regulatory process, from premarket submission, screening, review, and decision to postmarket surveillance. At the most recent consultation (June 10 and 11, 2004), Health Canada sought feedback on its Summary Basis of Decision (SBD), a first attempt at enhancing transparency. This document outlines the regulatory, safety, efficacy, and quality considerations that factor into a decision to grant market authorization for a drug or medical device. Templates have been tested, and pilot exercises can be viewed at ht tp ://www.hc-sc .gc .ca/hpfb-dgpsa/ tpd-dpt/ index_drugs_projects_e.html#sbd. However, the SBD may not add very much to the information already available to pharmacists in product monographs. Of note, the disclosure of negative outcomes or withdrawals (i.e., rejection of market authorization or submissions withdrawn by manufacturers before a decision by Health Canada) is not planned until phase 3 of the SBD implementation strategy, at an unspecified date. A timid attempt at transparency, some would argue, and certainly nothing like the situation that led to Louisiana’s bill banning low-slung pants that intentionally expose underwear — or more! Let’s wait and see how much Health Canada will reveal in the future.
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 | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.020 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.004 | 0.011 |
| Insufficient payload (model declined to judge) | 0.072 | 0.024 |
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".