Experience with external review panels to validate a large clinical pharmacy intervention study
Bibliographic record
Abstract
ABSTRACT Assessment of the true impact al pharmacists' interventions in pharmaceutical care is crucial to the justification of investment in resources for clinical pharmacy services. In a large study of clinical pharmacy interventions in hospitals, intervening pharmacists and attending physicians assessed the impact of the intervention on three aspects: therapeutic benefit, risks, and drug costs. The study showed that hospitals providing the highest level at pharmacotherapy monitoring made more interventions per patient and that, in these institutions, the impact was greater on therapeutic benefit and risk reduction. Both pharmacists and physicians caring for the patient had assessed the impact. It was deemed important to validate these results. External panels of academic clinical pharmacists and clinical pharmacology physicians were chosen to review a sampling of cases to determine their level of agreement with their professional colleague (pharmacist or physician) at the original site. Differences were found in the assessments for both the pharmacist panel reviewing the site pharmacists and the physician panel reviewing the site physicians. In general, the review panels tended to be less positive about therapeutic benefit and risk reduction, but similar about the impact on drug costs. Although validation exercises are desirable in such research, it remains to be established whether the expert panel approach is preferred to other methods such as the more arduous measurements of health outcomes, or even the subjective impression of the physician and pharmacist involved with the care of that patient.
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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 | 0.404 | 0.421 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".