Valuing biomarker diagnostics for dementia care: enhancing the reflection of patients, their care-givers and members of the wider public
Bibliographic record
Abstract
What is the value of an early (presymptomatic) diagnosis of dementia in the absence of effective treatment? There has been a lively scholarly debate over this question, but until now (future) patients have not played a large role in it. Our study supplements biomedical research into innovative diagnostics with an exlporation of its meanings and values according to (future) patients. Based on seven focusgroups with (future) patients and their care-givers, we conclude that stakeholders evaluate early diagnostics with respect to whether and how they expect it to empower their capacity to (self-) care. They value it, for instance, with respect to whether it (a) explains experienced complaints, (b) allows to start a process of psychological acceptance and social adaptation to the expected degeneration, (c) contributes to dealing with anxieties (with respect to inheritable versions of dementia), (d) informs adequately about when to start preparing for the end of life, (d) informs the planning of a request for euthanasia, or (e) allows society to deal with a growing amount of dementia patients. Our study suggests that information about disease is considered 'harmful' or 'premature' when recipients feel unable to act on that information in their (self-) care. The results of this research offers input to further ethical research. It invites to adopt a care perspective in evaluation and to seek ways to prevent the 'harm' that such diagnostic methods can bring about.
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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 | 0.098 | 0.128 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.017 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.003 | 0.022 |
| Research integrity | 0.006 | 0.012 |
| Insufficient payload (model declined to judge) | 0.003 | 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".