Complex calculations: ethical issues in involving at-risk healthy individuals in dementia research
Bibliographic record
Abstract
In dementia research evidence is mounting that therapeutic strategies that target moderate and even mild Alzheimer's disease may be missing the 'therapeutic window'. Given that the neuropathology that leads to Alzheimer's disease probably begins somewhere between 10 and 15 years before symptoms manifest, many believe that the optimal therapeutic strategy would target persons in the earliest phases of disease development or even earlier. This would include, for example, persons with prodromal Alzheimer's and even persons who are deemed at risk. Given the nature of research involving the central nervous system, it is conceivable that some therapeutic investigations may involve an increase over minimal risk. This paper examines how, in dementia research, at-risk persons, although healthy, bring multiple and intersecting vulnerabilities to the prospect of research participation even though they are clinically healthy. Current guidelines for research ethics may not provide adequately for the nuances of 'healthy individuals' and their possible vulnerabilities. In the context of neurodegenerative disease, the fact of being 'at risk' alters the vulnerability profile in significant ways. While healthy persons who are at risk of developing dementia may not appear to warrant placement in the research category of vulnerable participants (alongside prisoners, pregnant women and children) careful regard for the vulnerabilities that arise as a result of the intersecting circumstances of being healthy and at risk of an incurable disease are worthy of increased attention and consideration, particularly as the research effort for the increasingly prevalent disease of Alzheimer's moves forward.
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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 | 0.500 | 0.520 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.016 | 0.091 |
| Scholarly communication | 0.024 | 0.026 |
| Open science | 0.007 | 0.016 |
| Research integrity | 0.036 | 0.045 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".