Pilot study of a pharmaceutical care intervention in an outpatient lung transplant clinic
Bibliographic record
Abstract
BACKGROUND: Lung transplant recipients have complex drug regimens. Study objectives were to assess drug therapy problems (DTPs), pharmacist recommendations, and patient satisfaction with pharmacist services. METHODS: Using a pharmaceutical care assessment process, pharmacists identified DTPs and made therapeutic recommendations. Number of DTPs identified per pharmacist visit was calculated and compared to standard care visits through retrospective chart review. Potential clinical impact of recommendations was evaluated by blinded clinicians. Patient satisfaction was assessed via survey. RESULTS: Fifty-five DTPs were identified in 43 patients over 50 pharmacist visits (1.05 ± 1.34 DTPs per visit). In these same patients, rate of DTP identification was 0.51 ± 0.64 DTPs per standard visit in the preceding two-wk period (p = 0.018 vs. pharmacist visit). The most common DTPs identified by the pharmacist were adverse drug effect (27%) and untreated indication (25%). Overall, 62% of pharmacist recommendations were rated very significant or significant. Survey return rate was 58% and satisfaction scores ranged from 3 to 5 out of 5. Review of medications and teaching regarding the use of medications received the most "very satisfied" and "highly important" scores. CONCLUSIONS: Pharmacists can make valuable contributions in a lung transplant clinic setting by identifying DTPs and making recommendations with a positive impact on patient outcomes and satisfaction.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".