Cost-effectiveness of cannabinoids for pediatric drug-resistant epilepsy: protocol for a systematic review of economic evaluations
Bibliographic record
Abstract
BACKGROUND: Drug-resistant epilepsy negatively impacts the quality of life and is associated with increased morbidity and mortality and high costs to the healthcare system. Cannabis-based treatments may be effective in reducing seizures in this population, but whether they are cost-effective is unclear. In this systematic review, we will search for cost-effectiveness analyses involving the treatment of pediatric drug-resistant epilepsy with cannabis-based products to inform decision-making by public healthcare payers about reimbursement of such products. We will also search for cost-effectiveness analyses of other pharmacologic treatments for pediatric drug-resistant epilepsy, as well as estimates of healthcare resource use, costs, and utilities, for use in a subsequent cost-utility analysis to address this decision problem. METHODS: We will search the published and gray literature for economic evaluations of cannabis-based products and other pharmacologic treatments for pediatric drug-resistant epilepsy, as well as resource utilization and utility studies. Two independent reviewers will screen the title and abstract of each identified record and the full-text version of any study deemed potentially relevant. Study and population characteristics, the incremental cost-effectiveness ratio (ICER), as well as total costs and benefits, will be extracted, and quality will be assessed by use of the Drummond and CHEERS checklists; context-specific issues will also be considered. From model-based cost-utility and cost-effectiveness analyses, we will extract and summarize the model structure, including health states, time horizon, and cycle length. From resource utilization studies, we will extract data about the frequency of resource use (e.g., neurology visits, emergency department visits, admissions to hospital). From utility studies, we will extract the utility for each health state, the source of the preferences (e.g., child, parent, patient, general public), and the method of elicitation. DISCUSSION: Drug-resistant epilepsy in children is associated with important costs to the healthcare system, and decision-makers require high-quality evidence on which to base reimbursement decisions. The results of this review will be useful to both decision-makers considering the decision problem of whether to reimburse cannabis-based products through public formularies and to analysts conducting studies in this area. SYSTEMATIC REVIEW REGISTRATION: PROSPERO no.: CRD42018099591 .
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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 | 0.024 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.025 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| 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".