The Role of Clinical Pharmacist in Pediatrics’ Adherence to Antiepileptic Drugs
Bibliographic record
Abstract
Background: Rate of nonadherence to antiepileptic drugs (AEDs) in children is about 33%. Engaging clinical pharmacists in the management of patients has proved to increase adherence to medications which will improve the outcomes of treatment. Objectives: To investigate the effect of a clinical pharmacist-led education on the adherence to AEDs in pediatric patients with epilepsy. Secondary outcomes include effectiveness and safety of AEDs, satisfaction with information about AEDs provided to the caregivers, and patients quality of life (QoL). Methods: This was an interventional study where pediatric patients were randomly assigned to the intervention (n = 41) or the control (n = 40) group. A 30-minute clinical pharmacist-led educational interview to the parent/caregiver was provided to the first group as add-on to standard medical care received by latter. Outcomes were measured at baseline and after 8-week follow-up. Results: The intervention group had an increase in mean adherence score from 6 ± 1.09 at baseline to 7.6 ± 0.9 at follow-up ( P value < 0.001), while the control group had no significant change ( P value > 0.05), the difference between the 2 groups at follow-up was significant ( P value < 0.0001). No significant difference was observed between groups at follow-up with regard to effectiveness ( P value > 0.05), and safety ( P value = 0.08). While higher satisfaction with information ( P value < 0.0001), and higher QoL ( P value < 0.05) was observed in the intervention group. Conclusion and relevance: Clinical pharmacist-led education had a positive outcome on pediatric patients with epilepsy with regard to adherence, effectiveness, safety, satisfaction with information about AEDs, and QoL.
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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 | 0.002 | 0.012 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".