Seizing the moment: communicating ethics, decisions, and neurotechnological approaches to pediatric drug-resistant epilepsy
Bibliographic record
Abstract
It is a fundamental duty of neuroscientists to discuss the results of research and related ethical implications. Engagement with neuroscience is especially critical for families with children affected by disorders such as drug resistant epilepsy (DRE) as they navigate complex decisions about innovations in treatment that increasingly include invasive neurotechnologies. Through an evidence-based, iterative, and value-guided approach, we created the short-form documentary film, Seizing Hope: High Tech Journeys in Pediatric Epilepsy, to delve into the relationship between experts with first-hand, lived experience – youth with DRE and caregivers – and physician experts as they weigh medical and ethical trade-offs on this landscape. We describe the co-creation and evolution of this film, screenings, and feedback. Survey responses from 385 viewers highlight new developments in technologies for the treatment of DRE, how families navigate choices for treatment with brain technology, and a sense of hope for the future for children with epilepsy as key attributes of this science communication piece.
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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".