Improvement by Medication Less than Expected in Parkinson’s Disease: Blinded Evaluation of Levodopa Response
Bibliographic record
Abstract
Background: The latest Movement Disorder Society (MDS) diagnostic criteria require a good and sustained response to medication to get a diagnosis of Parkinson's disease, PD. Objective: The aim of this study was to evaluate levodopa response in a group of patients with probable PD, diagnosed by movement disorder specialists. Methods: An acute levodopa challenge test (LDCT) was performed after pausing the dopaminergic medication for 6 half-times. The motor part of the Unified Parkinson's Disease Rating Scale was performed in the OFF-state and after LDCT (ON). A good effect was defined as >30% improvement. A video-protocol was used to secure standardized motor examination with blinded assessments of the UPDRS-III OFF and ON. An age-matched group of control subjects (CS) was included but did not go through LDCT. All participants were evaluated with Montreal Cognitive Assessment (MoCA) and Beck's Depression Inventory (BDI). Results: In the statistical analysis, 37 patients were included. Twenty-one patients showed an improvement ≤30%, while 16 patients showed an improvement >30%. LDCT showed an overall mean improvement of 27.3% of motor UPDRS. In 43.2%, there was a discrepancy between the effect seen with the LDCT and the patients' self-perceived medicine evaluation. Patients with PD had a significantly lower MoCA score and more depressive symptoms compared to CS. Conclusions: We showed an acute effect of levodopa using LDCT that was around 30% improvement. While it lends support to the use of this limit in the MDS diagnostic criteria, an acute effect of less than 30% should be considered acceptable in some patients. Our study highlights a discrepancy in the objective measure of medicine effect on motor symptoms and the patient's subjective evaluation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".