Bibliographic record
Abstract
BACKGROUND: ), annually releases a list of drugs to avoid. The aim of this analysis was to review the status of the 2017 list of drugs in Canada to determine whether they had been approved for marketing, their therapeutic status and whether they have been recommended for listing on public drug plans. METHODS: . The status of each drug in Canada was assessed through the Drug Product Database. Therapeutic ratings were obtained from the Patented Medicine Prices Review Board (PMPRB) and the formulary listing recommendation came from the Common Drug Review (CDR) or the pan-Canadian Oncology Drug Review (pCODR). For drugs without a formulary recommendation the Ontario Drug Benefit (ODB) Formulary was searched to see if the product was listed. RESULTS: recommended not using 92 drugs. The PMPRB evaluated 36 of these drugs; 2 were classed as substantial improvements or breakthroughs, 3 as moderate improvements and 31 as little or no therapeutic improvement. Nine of the remaining drugs that were approved in Canada were not assessed because they were approved before 1988 (the year the PMPRB was established), 4 were approved from December 2015 onward and had not yet been reviewed by the PMPRB, and for 1 the approval date was unknown. Twenty-six of the drugs were evaluated by CDR or pCODR, of which 13 were recommended for formulary listing. Sixteen additional drugs that were not evaluated were on the ODB Formulary. INTERPRETATION: recommended avoiding were available in Canada. The results also highlight the diversity of the conclusions that different expert panels have reached.
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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.022 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".