Development of a preliminary essential medicines list for Canada
Bibliographic record
Abstract
BACKGROUND: Some evidence supports the use of a short list of essential medicines to improve prescribing. We aimed to create a preliminary essential medicines list for use in Canada. METHODS: The 2013 World Health Organization Model List of Essential Medicines was initially adapted by the research team. Fourteen Canadian clinicians gave suggestions for changes to the list. Literature relevant to each unique suggestion was gathered and presented to 3 clinician-scientists who used a modified nominal group technique to make recommendations on the suggested changes. Audits of prescriptions of 2 Toronto-based family health teams (an inner city clinic and a suburban site) between Aug. 1, 2013, and July 30, 2014, were performed to identify common prescriptions that were not on the draft list. Literature relevant to these additional medications was gathered and shared with the clinician-scientist review panel to determine whether each should be added to the list, and a list was developed. The audits were repeated based on the final list to provide a preliminary assessment of the coverage of the list. RESULTS: The multistep process produced a list of 125 medications. The medications included on this list covered 90.8% and 92.6% of prescriptions at the inner city clinic and the suburban site, respectively. In total, 93% of the patients seen at the inner city clinic and 96% of the patients seen at the suburban clinic had all or all but 1 of their medications covered by the list. INTERPRETATION: A preliminary list of essential medicines was developed that covered most, but not all, prescriptions at 2 primary care sites. The list should be further refined based on wider input.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".