Novel Neurotechnological Interventions for Pediatric Drug-Resistant Epilepsy: Physician Perspectives
Bibliographic record
Abstract
This qualitative study investigated factors that guide physicians' choices for minimally invasive and neuromodulatory interventions as alternatives to conventional surgery or medical management for pediatric drug-resistant epilepsy. North American physicians were recruited to one of 4 focus groups at national conferences. Discussions were analyzed using qualitative content analysis. A pragmatic neuroethics framework was applied to interpret results. Discussions revealed 2 major thematic branches: (1) clinical decision making and (2) ethical considerations. Under clinical decision making, physicians emphasized scientific evidence and patient candidacy when assessing neurotechnologies for patients. Ongoing seizures without intervention was important for safety and neurodevelopment. Under ethical considerations, resource allocation, among other financial considerations for technology adoption, were considerable sources of pressure on decision making. Access to neurotechnology was a salient theme differentiating Canadian and American contexts. When assessing novel neurotechnological interventions for pediatric drug-resistant epilepsy, physicians balance clinical and ethical factors to guide decision making and best practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".