Demonstrating value, documenting care: Lessons learned about writing comprehensive patient medication assessments in the IMPACT project: PART II
Bibliographic record
Abstract
Summary of recommendations1. Suggest decrease dose of XX [name of drug] to 150 mg once daily given low creatinineclear ance of 40 mL/min.2. Suggest tapering XX [name of drug] to X mg[dose] at bedtime for 1 week, then stoppingalt ogether (have discussed with patient; sheis willing to start today if you agree).3. Mrs. Y has agreed to stop XX [name of drug](which may have been contributing to recenthigh BP) and will monitor BP daily at hometo make sure it decreases to <130/80 mm HgBOX 1 The IMPACT experience The IMPACT project was a large-scale demonstration project funded by the Ontario Primary Health Care Transition FundProject in which 7 nondispensing pharmacists were integrated into 7 different family physician group practices between June2004 and July 2006. The IMPACT pharmacists worked approximately 2.5 days per week over the 2-year period conductingcomprehensive medication assessments for patients, providing drug information and education for health care providers,and implementing approaches to optimize drug prescribing and use within the practice.
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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 | 0.059 | 0.123 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".