Analysis on executive dysfunction of patients with multiple system atrophy and Parkinson's disease
Bibliographic record
Abstract
Objective To explore the characteristics of executive dysfunction of patients with multiple system atrophy (MSA) and Parkinson's disease (PD) by neuropsychological tests. Methods Mini-Mental State Examination (MMSE), Montreal Cognitive Assessment (MoCA), Stroop Color-Word Test (SCWT), Digital Symbol Substitution Test (DSST)/Graphic Symbol Substitution Test (GSST), Clock Drawing Test (CDT) and Trail Making Test (TMT) were used to assess the overall cognitive and executive function of 34 patients with MSA [21 with cerebellar-predominant (MSA-C) and 13 with parkinsonism-predominant (MSA-P)], 18 patients with primary PD and 14 normal controls. Results There was significant difference in MoCA score among different groups ( P = 0.019). PD and MSA-C groups had lower MoCA score than that in normal control group ( P = 0.015, 0.002). There were significant differences in each SCWT score ( P = 0.035, 0.013, 0.012, 0.037), DSST ( P = 0.000), GSST ( P = 0.000) and TMT ( P = 0.035) among different groups. Among them, MSA-C and MSA-P groups had significantly higher SCWT-A ( P = 0.004, 0.045), SCWT-B ( P = 0.001, 0.036) and SCWT-D scores ( P = 0.023, 0.010) than those in normal control group. PD, MSA-C and MSA-P groups had significantly higher SCWT-C ( P = 0.005, 0.014, 0.003), DSST ( P = 0.003, 0.000, 0.000) and GSST scores ( P = 0.001, 0.000, 0.000) than those in normal control group. MSA-P group had significantly higher TMT score than that in normal control group ( P = 0.006). Conclusions Patients with MSA and PD may present executive dysfunction to different degrees. SCWT and DSST/GSST tests are useful in assessing executive dysfunction in those patients. DOI: 10.3969/j.issn.1672-6731.2016.05.006
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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 | 0.000 | 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".