Application of Montreal cognitive assessment in screening cognitive impairment in Parkinson's disease patients
Bibliographic record
Abstract
Objective To study the application of Montreal cognitive assessment(MoCA) and minimental state examination(MMSE) in screening cognitive impairment in Parkinson's disease(PD) patients.Methods One hundred and twenty-nine PD patients at the age≥60 years were divided into normal group,mild cognitive impairment(MCI)group and PD dementia(PDD)group according to their cognitive function.They were assessed and analyzed according to their MoCA and MMSE score.Results The MoCA score was significantly different in 3 groups(P0.01).The scores of drawing cube,retelling,counting animals in 1 min,similarity anddelayed recallwere lower in MCI and PDD groups than in normal group(P0.ODwhile the scores of naming,digit span andorientationwere higher in normal and MCI groups than in PDD group(P0.05).In addition,the area under ROC for the patients was 0.803 for the diagnosis of MCI according to MMSE,0.803 for the diagnosis of MCI according to MMSE,0.947 for the diagnosis of MCI according to MoCA,0.952 for the diagnosis of PDD according to MMSE and 0.990 for the diagnosis of PDD according to MoCA.Conclusion MoCA can be used as an effective tool for screening the cognitive impairment in PD patients.The MoCA score decreases gradually with the aggravation of PD.The MoCA optimal cutoff value is≤23 score for screening MCI in PD and the sensitivity of MoCA is higher than that of MMSE in screening PD patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".