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Record W4394203208 · doi:10.6084/m9.figshare.21076408

Dance for Parkinson's. A dataset of a Greek pilot clinical trial.

2022· dataset· en· W4394203208 on OpenAlexaboutno aff
Michail Elpidoforou, Leonidas Stefanis, Μαριάννα Δρακοπούλου, Anna Kavga, Chrysa Chrysovitsanou, Daphne Bakalidou

Bibliographic record

VenueFigshare · 2022
Typedataset
Languageen
FieldMedicine
TopicParkinson's Disease Mechanisms and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDancePsychologyParkinson's diseasePhysical medicine and rehabilitationMedicineArtInternal medicineVisual arts

Abstract

fetched live from OpenAlex

This dataset (from clinical trial: Effects of a structured dance program in Parkinson’s disease. A Greek pilot study) includes raw data extracted from 16 early-to-mid-stage Parkinson's disease patients that met the inclusion criteria for the pilot clinical trial. Clinical assessments of quality of life (Parkinson's Disease Questionnaire-8), depressive symptoms (Beck Depression Inventory-II), fatigue (Parkinson Fatigue Scale-16), cognitive functions (Montreal Cognitive Assessment), balance (Berg Balance Scale), and body mass index (kg/m2) performed before (baseline) and after the sixteen 60-min dance (Dance for Parkinson's®) intervention of 8 weeks (2/w).

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.028
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.057
Threshold uncertainty score0.191

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.028
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0030.002
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.0570.020

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 head0.195
GPT teacher head0.406
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2022
Admission routes1
Has abstractyes

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