Medication recommendations to physicians by pharmacists for seniors: expected clinical impact in relation to implementation and expected time frame to effect
Bibliographic record
Abstract
Abstract Aims and objectives To describe recommendations made by pharmacists in a trial that had found no improvements in selected clinical outcomes (the Seniors Medication Assessment Research Trial, SMART) in terms of expected impact on clinical outcomes and whether they had been implemented by the end of the 5-month period of follow-up. Setting SMART was conducted in a non-academic community practice setting. Method Recommendations made by the pharmacists during SMART, a cluster-randomised controlled trial conducted in family physician offices, were evaluated in this descriptive study. All recommendations to physicians were evaluated independently by two assessors using criteria established a priori and without knowledge of patient outcomes. Each recommendation was evaluated on likely strength and time to impact the patient's health and whether the recommendation was based on published evidence. Relationships between these criteria were analysed. Key findings Overall, the pharmacists made 1099 recommendations for 431 patients randomly assigned to the intervention group or a mean of 2.6 recommendations (standard deviation, 2.1) per patient. A moderate or marked impact on patient health within the 5-month follow-up period would have been expected for 15.5% of all recommendations. At study end, physicians fully implemented 45.8% of the recommendations. Among the recommendations that had been fully implemented, 64.5% of those expected to have a marked impact and 27.5% of those expected to have a moderate impact were anticipated to have the effect on patients' health beyond the 5-month period of follow-up reported in the study results. Conclusion It is likely that one of the contributing factors to not finding statistically significant differences in the SMART study was that only a small proportion of recommendations (15.5%) made by the SMART pharmacists would have an expected clinical effect within the study's follow-up period.
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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 | 0.056 | 0.296 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".