4CPS-097 Role of the pharmacist in the care of the lung transplant patient
Bibliographic record
Abstract
<h3>Background and importance</h3> After a lung transplant, patients must have knowledge about pharmacological treatment and healthy lifestyle habits. <h3>Aim and objectives</h3> To evaluate the effectiveness of pharmaceutical care for lung transplant patients from 2017 to 2020 aimed at increasing their knowledge about pharmacological treatments and healthy lifestyles and its influence on unscheduled readmissions in the first 90 days after transplantation. <h3>Material and methods</h3> 129 lung transplant patients received, by the transplant pharmacist, information sessions 2 or 3 weeks after transplantation, during the hospital stay, on medicines and healthy lifestyles and the delivery of an informative book prepared for the occasion by the multidisciplinary team. The knowledge acquired was evaluated with the completion of a questionnaire before and after the information sessions. The score of their satisfaction with the information received before and after the sessions was also collected. Fifteen days after discharge, the patients were telephoned and a further survey was carried out. The reasons for unscheduled readmissions in the period were mainly worsening of the respiratory function test and fever. <h3>Results</h3> Of the 129 lung transplanted patients in the period studied, 114 completed both questionnaires. 70 were men and the average age was 55 years. The causes of transplantation were: diffuse interstitial pulmonary disease: 58, chronic obstructive pulmonary disease: 29; bronchiectasis/cystic fibrosis: 17; other causes: 7; pulmonary hypertension: 3. 99 transplants were double-lung, 20 left single-lung and 10 right single-lung. The percentage of correct answers varied from 70% in the pre-questionnaire to 85% in the post-questionnaire (p=0.000). The score of the patients’ satisfaction with the information received was 6.6 points in the pre-questionnaire and 9.3 in the post-questionnaire (p=0.000). 114 telephone calls were made to patients’ homes in order to complete the post-questionnaire. In the first 90 days after transplantation, 27% of the transplanted patients were readmitted unscheduled in 2020 compared with 38.2% in 2016 (control group) (p=0.225). <h3>Conclusion and relevance</h3> Pharmaceutical care for lung transplant patients statistically significantly improved their knowledge about medicines and healthy lifestyles as well as their degree of satisfaction with the information received, and statistically significantly improved the unscheduled readmissions in the first 90 days after transplantation. <h3>References and/or acknowledgements</h3> <h3>Conflict of interest</h3> No conflict of interest
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".