Ethics of neuroimaging after serious brain injury
Bibliographic record
Abstract
BACKGROUND: Patient outcome after serious brain injury is highly variable. Following a period of coma, some patients recover while others progress into a vegetative state (unresponsive wakefulness syndrome) or minimally conscious state. In both cases, assessment is difficult and misdiagnosis may be as high as 43%. Recent advances in neuroimaging suggest a solution. Both functional magnetic resonance imaging and electroencephalography have been used to detect residual cognitive function in vegetative and minimally conscious patients. Neuroimaging may improve diagnosis and prognostication. These techniques are beginning to be applied to comatose patients soon after injury. Evidence of preserved cognitive function may predict recovery, and this information would help families and health providers. Complex ethical issues arise due to the vulnerability of patients and families, difficulties interpreting negative results, restriction of communication to "yes" or "no" answers, and cost. We seek to investigate ethical issues in the use of neuroimaging in behaviorally nonresponsive patients who have suffered serious brain injury. The objectives of this research are to: (1) create an approach to capacity assessment using neuroimaging; (2) develop an ethics of welfare framework to guide considerations of quality of life; (3) explore the impact of neuroimaging on families; and, (4) analyze the ethics of the use of neuroimaging in comatose patients. METHODS/DESIGN: Our research program encompasses four projects and uses a mixed methods approach. Project 1 asks whether decision making capacity can be assessed in behaviorally nonresponsive patients. We will specify cognitive functions required for capacity and detail their assessment. Further, we will develop and pilot a series of scenarios and questions suitable for assessing capacity. Project 2 examines the ethics of welfare as a guide for neuroimaging. It grounds an obligation to explore patients' interests, and we explore conceptual issues in the development of a quality of life instrument adapted for neuroimaging. Project 3 will use grounded theory interviews to document families' understanding of the patient's condition, expectations of neuroimaging, and the impact of the results of neuroimaging. Project 4 will provide an ethical analysis of neuroimaging to investigate residual cognitive function in comatose patients within days of serious brain injury.
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 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 | 0.014 | 0.230 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".