Health utilities of patients with epilepsy in a Canadian population
Bibliographic record
Abstract
OBJECTIVE: Health state utilities are required to obtain quality adjusted life years, a common metric that informs clinical decision-making at individual, group, and health policy levels. Health state utilities are different from health-related quality of life, and their distribution across patients with epilepsy, as well as the factors that impact them, have not been studied in depth. We aimed to describe the distribution of health state utilities in people with epilepsy and the impact of different combinations of clinical and demographic factors on health state evaluation. METHODS: We performed a retrospective analysis of patients' data prospectively collected in the Calgary Comprehensive Epilepsy Program registry. Patient-reported health state utilities were measured using the 5-level EuroQol 5-Dimension scale (EQ-5D-5L) completed at their initial assessment. EQ-5D-5L index scores were derived via the time trade-off approach based on Canadian norms, and their distribution across different health states and patient characteristics was obtained. The Tobit regression model was used to evaluate the determinants of EQ-5D-5L index scores. RESULTS: Of 1446 patients included in this analysis, 724 (50.5%) were female. The median (interquartile range) Canada-normed EQ-5D-5L index score was .87 (.71-.91). Patients with significantly lower health utilities were more likely to be female (p = .008), to be older (p = .034), to be unmarried (p = .013), to have failed to achieve 1-year seizure freedom (p < .001), to have no postsecondary education (p = .028), to be depressed (p < .001), to have antiseizure medication side effects (p = .001), to be unemployed (p < .001), and to be unable to drive (p < .001). A look-up table of health utilities based on combinations of clinical-demographic characteristics was produced. SIGNIFICANCE: Health utility estimates for combinations of different health states in people with epilepsy attending specialty clinics are now available. These can help guide clinical decision-making in routine clinical practice, economic evaluations of treatment interventions, and health care policies.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".