Understanding Attributes that Influence Physician and Caregiver Decisions About Neurotechnology for Pediatric Drug-Resistant Epilepsy: A Formative Qualitative Study to Support the Development of a Discrete Choice Experiment
Bibliographic record
Abstract
OBJECTIVE: This study reports formative qualitative research used to analyze decision making regarding neurotechnological interventions for pediatric drug-resistant epilepsy from the perspective of physicians and caregivers and the derivation of attributes for a discrete choice experiment. METHODS: Purposive and convenience sampling was used to recruit physicians and caregivers. Physician focus group sessions were held at key national conferences in the USA and Canada. Caregivers were approached through clinics with established epilepsy surgery programs in the USA and Canada. Thematic analysis was used to identify critical features of decisions about treatment outcomes, procedural trade-offs, values, and concerns surrounding conventional and novel pediatric drug-resistant epilepsy interventions among physicians and caregivers. RESULTS: The results highlight the presence of central attributes that are considered by both groups in decision making, such as "chances of seizure freedom", "risk", "availability of evidence", and "cost to families", as well as attributes that reflect important differences between groups. Physicians were focused on the specifics of treatment options, while caregivers thought more holistically, considering the overall well-being of their children. DISCUSSION: The findings shaped the development of a discrete choice experiment to understand the likely uptake of different neurotechnologies. We identified differences in decision making and thus designed two discrete choice experiments to elicit preferences for pediatric drug-resistant epilepsy treatments, one aimed at clinicians and one at caregivers. The variation we observed highlights the value of seeking to understand the influences at the point of clinical decision making and incorporating this information into care.
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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 | 0.065 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".