Living with Brain Data: Collaboration and Equity in Data-Intensive Brain Implants
Bibliographic record
Abstract
This paper examines the lived experience of implanted medical devices through the case of brain implants for epilepsy. These data-driven devices record brain signals to detect and interrupt seizures, introducing new forms of technology-mediated care. Drawing on interviews with 17 patients and caregivers, we examine how data-intensive implants reshape medical interactions and everyday life. Participants reported shifts in doctor-patient collaboration, including the integration of a new expert-an engineer responsible for device-related concerns-into clinical visits. The preparatory and ongoing work of data transfer posed challenges for participants who were low-income, aging, traveling, or busy. Participants expressed a strong desire to access implant data to better understand and manage their condition. They were satisfied with the device unless their medications and/or seizures increased. We discuss emerging considerations for collaborative care and design justice introduced by medical implants that, unlike wearables, deliver treatment and cannot be easily set aside.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".