Pharmaceutical recommendations in a university hospital transplant unit
Bibliographic record
Abstract
Introduction: The transplanted patient has a complex pharmacotherapy, with the pharmacist having an important role in the multidisciplinary team. Objective: To analyze the pharmaceutical recommendations made during the hospitalization of the patients in kidney and liver transplant units. Methods: This was a cross-sectional study in which pharmaceutical recommendations from May 2017 to April 2018 were collected from the records contained in the database of the Clinical Pharmacy Unit of a University Hospital in Fortaleza, Brazil. The recommendations were categorized and analyzed based on the classification used in the institution. Results: There were 1241 pharmaceutical recommendations involving 325 patients and 1466 medications. The recommendations were more frequent during liver transplantation (54.2%, n = 672), with dose adjustments (18.2%, n = 122) and dilution / reconstitution (9.8%, n = 66) being the most predominant types. In kidney transplantation, recommendations for education about medication use (17.6%, n = 100) and treatment adherence strategies (17.6%, n = 100) were the most predominant. The most frequent therapeutic classes were systemic antibacterials (31.2%, n = 458) and immunosuppressants (25.1%, n = 368). The acceptance rate of recommendations for kidney and liver transplantation were 95.1% (n = 541) and 95.4% (n = 641), respectively. Conclusions: The present study showed a high frequency of pharmaceutical recommendations and these results demonstrate that the detection of drug-related problems generates pharmaceutical recommendations that can contribute to the reduction of negative drug-associated results and increase patient safety.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".