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Record W3015272030 · doi:10.1212/wnl.0000000000009400

Patients' views on the ethical challenges of early Parkinson disease detection

2020· article· en· W3015272030 on OpenAlexaff
Eva Schaeffer, Annette Rogge, Katharina Nieding, Vera Helmker, Christa Letsch, Björn Hauptmann, Daniela Berg

Bibliographic record

VenueNeurology · 2020
Typearticle
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsCentre for Movement Disorders
Fundersnot available
KeywordsParkinson's diseaseDiseaseMedicinePsychologyPsychiatryPathology

Abstract

fetched live from OpenAlex

OBJECTIVE: To evaluate the point of view of patients with Parkinson disease (PD) on early detection and risk disclosure in the prodromal phase of PD and to derive recommendations for an ethical framework for the recruitment of prodromal PD cohorts. METHODS: A standardized questionnaire to evaluate the patients' perception on early diagnosis in PD was designed by an interdisciplinary study group. After testing in a preliminary feasibility study (n = 20), the survey was performed retrospectively with patients from our clinic. RESULTS: A total of 101 patients with PD answered the questions. The majority of patients reported that time from onset of motor symptoms to diagnosis was burdensome, including false diagnoses and many consultations of various medical specialists. However, most of the patients evaluated early risk disclosure with skepticism. Freedom of choice and the potential of changes in lifestyle were rated as important. CONCLUSION: Although patients with PD reported the time to diagnosis retrospectively as burdensome, the majority was skeptical regarding early disclosure of risk, especially with regard to the lack of pharmacologic options. Circumstances under which early detection and disclosure would have been approved by the majority of patients were (1) advice on lifestyle changes (exercise, nutrition) as potentially disease course-modifying therapy; (2) the establishment of an early diagnosis "culture," including early clarification of the patients' wish to know; and (3) regular support and follow-up of individuals after risk disclosure.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.513
Threshold uncertainty score0.289

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.048
GPT teacher head0.275
Teacher spread0.226 · 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

Citations52
Published2020
Admission routes1
Has abstractyes

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