Retrospective evaluation of prescribing pattern and utilization of antiepileptic drugs in pediatric, neurosurgery, and psychiatry wards: A comparative study to the standard treatment guidelines
Bibliographic record
Abstract
Antiepileptic drugs (AED) are progressively utilized for off-label conditions other than epilepsy, like bipolar disorder and migraine. The objective of this study was to evaluate current prescribing patterns and utilization of AED in pediatric, neurosurgery, and psychiatry wards and to compare them to the standard treatment guidelines. A descriptive, cross-sectional study was conducted in Ayyub Teaching Hospital, Abbottabad from December 1st, 2018 to April 2019. Data on demographic and clinical characteristics, utilization patterns of AED, adherence to standard treatment guidelines, and frequency of potential drug-drug interactions were analyzed using descriptive statistics. Among 410 patients, 54.3% (n = 223) were male, 45.6%(n = 187) were female, and 63.7% (n = 261) were from the 1 to 18 years' age group. The majority 47.3% (n = 194) were from the pediatric ward followed by neurosurgery 28.7%(n = 118). Among the studied patients, 96.1% of them had comorbid conditions other than epilepsy alone. With regards to types of seizures unclassified seizures were the most common seizure type (59.8%; n = 245) followed by generalized tonic clonic seizures 23.4% (n = 96). In this study, the most frequently utilized AED was sodium valproate 59.0% (n = 242) followed by antiepileptic first-generation medicines were commonly used (76.3%). Although a total of 77.6% of the patients showed nonadherence to National Institute for Health and Care Excellence guidelines and 87.6% of them showed drug interactions. Findings from this study showed prescription patterns and utilization of AED in patients with epilepsy and non-epilepsy disorders which may help healthcare providers in making accurate clinical decisions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".