A Scoping Review of Ethical and Legal Issues in Behavioural Variant Frontotemporal Dementia
Bibliographic record
Abstract
Behavioural variant frontotemporal dementia (bvFTD) is a subtype of frontotemporal dementia characterized by changes in personality, social behaviour, and cognition. Although neural abnormalities cause bvFTD patients to struggle with inhibiting problematic behaviour, they are generally considered fully autonomous individuals. Subsequently, bvFTD patients demonstrate understanding of right and wrong but are unable to act in accordance with moral norms. To investigate the ethical, legal, and social issues associated with bvFTD, we conducted a scoping review of academic literature with inclusion & exclusion criteria and codes derived from our prior work. Among our final sample of fifty-six articles, four mentioned bvFTD patient-offenders as unfit to stand trial by insanity, and sixteen mentioned the use of dementia evidence in a court of law to better understand the autonomy of bvFTD patients. Additional emergent issues that were discovered include: training police officers to handle situations involving bvFTD patients and educating healthcare providers on how to help caregivers navigate bvFTD. The current literature highlights the inadequacy of traditional applications of medico-legal categories such as autonomy, capacity and competence, in informing cognitive capacity assessments in clinical and legal settings and deserves consideration by neuroethicists.
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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 | 0.010 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.016 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".