Balancing health literacy about epilepsy surgery in the community
Bibliographic record
Abstract
OBJECTIVE: Despite significant underutilization of surgical treatment for drug-resistant epilepsy, no studies have quantified patient desire for surgery within a representative population. The main objective was to determine desire for surgery in a sample with a high proportion of potential candidates to characterize patient-related barriers to the treatment. Secondary objectives included assessing clinical predictors of attitudes toward surgery and evaluating the impact of passive knowledge translation on desire for surgery. METHODS: An online survey was administered to all clients connected with a core epilepsy community access center. It obtained information about demographics, clinical characteristics, knowledge of epilepsy surgery, and interest in receiving surgery before and after receiving risk/benefit information about it. RESULTS: Of 118 potential respondents, 48 (41%) completed the questionnaire, of which 67% had failed more than two AEDs and 78% experienced seizures in the past year. Eleven (26%) were uninterested in receiving surgery at baseline, and this decreased significantly to 7 (16%) following knowledge translation regarding the benefits (p = 0.001). Significance was lost with subsequent complication rate information despite fewer respondents still being uninterested compared to baseline (20% vs. 26%). Having experienced seizures within the past month was correlated with being interested in or undecided regarding surgery at baseline and following all steps of knowledge translation. Subjects had conservative views regarding the benefits of surgery and largely overestimated the risks. SIGNIFICANCE: A significant portion of those with active epilepsy in the community do not desire surgical treatment. Passive knowledge translation regarding the risks and benefits enhanced optimistic attitudes and mobilized interest within a subset of participants. Preexisting views regarding the risks of surgery were exaggerated, and analysis suggests that these views can be modified with information about the benefits of surgery. However, exaggerated risk perceptions return following crude descriptions of the risks, underlying the importance of sensitive counseling from primary care physicians.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".