Influencing factors and prediction model construction of cognitive impairment in young⁃old patients with Parkinson's disease
Bibliographic record
Abstract
ObjectiveTo investigate the risk factors for cognitive impairment in the young⁃old patients with Parkinson's disease（PD），and to construct a prediction model based on the risk factors.MethodsA total of 164 young⁃old PD patients who completed the 5⁃year follow⁃up from PPMI database were included in this study.Montreal Cognitive Assessment（MoCA） was used to assess cognitive function. 164 young⁃old PD patients were divided into cognitive normal group（PD⁃NC） and cognitive impairment group（PD⁃CI）.Logistic regression analysis was used to explore the risk factors for cognitive impairment in young⁃old patients with PD，and a predictive model was constructed based on these factors.ResultsAmong 164 young⁃old PD patients，101 were in the PD⁃NC group and 63 were in the PD⁃CI group， and the incidence of cognitive impairment was 38.4%.Logistic regression analysis showed that age，years of education，MDS⁃UPDRSⅡ and MDS⁃UPDRS Ⅲ scores entered the regression equation.The AUC of the constructed prediction model was 0.815.ConclusionsThe cognitive function of young⁃old patients with PD is affected by age，years of education， activities of daily living and motor function.The established prediction model has good discrimination and calibration，which can provide a reference for early screening and intervention of cognitive impairment in young⁃old patients with PD.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".