Low-dose anticholinergic therapy causes cognitive impairment in Parkinson's disease patients
Bibliographic record
Abstract
Background: Before L-Dopa’s discovery, anticholinergic drugs were among the first treatments for Parkinson’s disease. Only now trihexyphenidyl (THP) is approved to treat unresponsive L-dopa tremors in young, cognitively unaffected Parkinson’s disease patients. However, there are no specific recommendations for disease duration, medication dose, or cognitive status. In low-income countries, THP is still frequently used in Parkinson’s disease patients with tremor. The objective of the current study was to evaluate cognitive performance in Parkinson’s disease patients receiving a low dose of THP. Material and methods: The study was performed on nineteen PD patients, nine of whom were on THP. All patients completed MoCA cognitive assessment. The patients were matched depending on their age, disease severity based on UPDRS III and duration of the disease. Results: The THP patients were taking an average dose of 3.3 mg of THP daily for an average of 1.8 years. There were no statistical differences between THP patients and non-THP patients in age (64.8± 4.8 vs 67.2±6.9, p=0.4), UPDRS III (32.1±8.9 vs 41.5±20.6, p=0.2) and disease duration (6.2±4.9 vs 7.0 ± 4.0, p=0.7). The THP patients had lower cognitive performance, with a total MoCA of 19.22 ± 3.3 vs. non-THP patients 24.2±3.0, p=0.003. Conclusions: In Parkinson’s disease patients, even a low dose of THP causes significant cognitive loss.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".