Observation on clinical efficacy of pramipexole tablets combined with madopar to treat Parkinson's disease
Bibliographic record
Abstract
OBJECTIVE To observe the clinical efficacy and safety of pramipexole tablets combined with madopar tablets to treat Parkinson's disease.METHODS A total of 156 patients with Parkinson's disease,who visited our hospital in January2010-December 2012,were divided into observation group and control group.Cases in control group received madopar tablets,and cases in observation group received pramipexole tablets combined with madopar tablets.The adverse reactions and improvement of motor symptoms and non-motor symptoms in both groups were compared after treatment.RESULTS(1)The United Parkinson Disease Rate Scale(UPDRS)in control group was not significantly improved after treatment,but UPDRS in observation group was significantly improved after treatment(P0.05 or P0.01),which was significantly different from those in control group(P0.05 or P0.01).(2)The scores of Mini Mental State Examination(MMSE),Montreal Cognitive Assessment(MoCA),Rey Auditory Verbal Learning Test(RAVLT),Symbol Digit Modalities Test(SDMT),and Wechsler Intelligence Scale-block design(BD)in control group were not significantly improved after treatment,but the scores of MMSE,MoCA,RAVLT,SDMT,and BD in observation group were significantly improved after treatment(P0.01),which were significantly different from those in control group(P0.01).(3)No serious adverse reactions occurred in both groups,and the rate of adverse reactions in observation group was significantly less than that in control group(P0.05).CONCLUSIONPramipexole tablets combined with madopar tablets has favorable effects to treat Parkinson's disease,and can significantly improve motor symptoms and non-motor symptoms,and results in less adverse reactions.
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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 | 0.001 | 0.001 |
| 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 | 0.000 | 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".