A prototype for evidence-based pharmaceutical opinions to promote physician-pharmacist communication around deprescribing
Bibliographic record
Abstract
CONTEXT: Interprofessional communication is an effective mechanism for reducing inappropriate prescriptions among older adults. Physicians' views about which elements are essential for pharmacists to include in an evidence-based pharmaceutical opinion for deprescribing remain unknown. OBJECTIVE: To develop a prototype for an evidence-based pharmaceutical opinion that promotes physician-pharmacist communication around deprescribing. METHODS: A standardized template for an evidence-based pharmaceutical opinion was developed with input from a convenience sample of 32 primary care physicians and 61 primary care pharmacists, recruited from conferences and community settings in Montreal, Canada. Participants were asked to comment on the need for clarifying treatment goals, including personalized patient data and biomarkers, highlighting evidence about drug harms, listing the credibility and source of the recommendations, providing therapeutic alternatives and formalizing official documentation of decision making. The content and format of the prototype underwent revision by community physicians and pharmacists until consensus was reached on a final recommended template. RESULTS: The majority of physicians (84%-97%) requested that the source of the deprescribing recommendations be cited, that alternative management options be provided and that the information be tailored to the patient. Sixteen percent of physicians expressed concern about the information in the opinions being too dense. Pharmacists also questioned the length of the opinion and asked that additional space be provided for the physician's response. A statement was added making the opinion a valid prescription upon receipt of a signature from physicians. Compared to a nonstandardized opinion, the majority of pharmacists believed the template was easier to use, more evidence based, more time efficient and more likely to lead to deprescribing. CONCLUSION: 2018;151:xx-xx.
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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 | 0.039 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 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".