A longitudinal cohort study on the impact of the clobazam shortage on patients with epilepsy
Bibliographic record
Abstract
OBJECTIVE: Drug shortages are occurring at an increasing rate. From May to October 2016, there was a shortage of a level I critical antiepileptic drug, clobazam. We aimed to study the impact of this shortage on patients with epilepsy. METHODS: Adult patients from Calgary's Comprehensive Epilepsy Program who were taking clobazam were approached to participate in the study. Baseline data from the clinic prospective registry included clinical variables and presurvey patient-reported outcomes (PROs) such as the Global Assessment of Severity of Epilepsy, the Global Assessment of Disability Associated with Seizures, and the Quality of Life in Epilepsy questionnaire. We used a mixed-methods cross-sectional questionnaire via telephone. We analyzed quantitative data using descriptive methods and qualitative data using a phenomenological approach. RESULTS: Of the 85 eligible patients, 84% (n = 71) agreed to participate. Participants ranged from 18 to 78 years old, and 59.2% of participants were female. Overall, 80% of participants experienced some form of medication change due to the shortage. No significant differences were found in the pre- and during-shortage PRO data. However, 6 important themes emerged regarding the impact of the antiepileptic drug shortage on patients: (1) communication and awareness, (2) burden on patients and caregivers, (3) physical impact, (4) psychological impact, (5) health care provider assistance in navigating the shortage, and (6) policy change. SIGNIFICANCE: We examined the impact of the clobazam shortage on patients with epilepsy from the patient's perspective and identified 6 important themes. As a result, we were able to gain insight into what patients need to cope with drug shortages and make recommendations that can help mitigate the impact of shortages on patients in the future. Further research is needed to better understand drug shortages from the patient's perspective in various settings and conditions.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".