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Record W2913427366 · doi:10.1007/s11019-018-09883-2

Valuing biomarker diagnostics for dementia care: enhancing the reflection of patients, their care-givers and members of the wider public

2019· article· en· W2913427366 on OpenAlexfundno aff
Floris H.B.M. Schreuder, Catharina J.M. Klijn, Marcel M. Verbeek

Bibliographic record

VenueMedicine Health Care and Philosophy · 2019
Typearticle
Languageen
FieldMedicine
TopicEthics in Clinical Research
Canadian institutionsnot available
FundersAlzheimer NederlandZonMwChinese Academy of Agricultural SciencesWeston Brain Institute
KeywordsPhilosophy of medicineDementiaHarmMedical lawValue (mathematics)PsychologyPhilosophy of biologyDiseasePerspective (graphical)MedicinePublic relationsSocial psychologyPsychiatryAlternative medicinePolitical sciencePhilosophy of scienceEpistemologyPathology

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.251
Threshold uncertainty score0.476

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.197
GPT teacher head0.471
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2019
Admission routes1
Has abstractyes

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