Data on the effect of Parkinson's disease multimodal complex treatment in a German University Hospital
Bibliographic record
Abstract
This article presents demographic and detailed clinical data from 159 patients with Parkinson's disease or atypical Parkinsonian syndromes treated in the Parkinson's disease multimodal complex treatment (PD-MCT) from 01.01.2019 until 31.12.2019 at the Department of Neurology of the University Hospital Jena, Germany. At baseline, the following variables were collected: age, sex, diagnosis, phenotype, disease duration, Hoehn and Yahr stage, Movement Disorder Society sponsored revision of the unified Parkinson's disease rating scale (MDS-UPDRS) part I-IV, levodopa equivalent daily dose (LEDD), Tinetti test, nonmotor symptoms questionnaire (NMSQ), Montreal Cognitive Assessment (MoCA), measures of depressive symptoms using the Hospital Anxiety and Depression Scale (HADS-D) and the Beck Depression Inventory (BDI-II), health-related quality of life assessed by the Short-Form Health Survey (SF-12), and the treatment duration according to the Operation and Procedure Classification System. To assess the short-term effect of PD-MCT, the MDS-UPDRS III, Tinetti test, and LEDD were collected again at discharge from hospital. One month after discharge, a first follow-up was conducted and patients rated their general condition. One year after discharge, a second follow-up was conducted and the SF-12 was collected. The dataset allows determination of the effect of PD-MCT and identification of predictors of a beneficial treatment. The dataset can be used by clinicians and academia for further research and as reference. The dataset can also be used in a large range of other topics where demographic and clinical parameters of the PD-MCT are relevant. The data presented herein is associated with the research article "Short- and Long-Term Effect of Parkinson's Disease Multimodal Complex Treatment" [1] and available on Mendeley Data [2].
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".