Nomogram to predict 5-year global cognitive impairment in de novo Parkinson disease with normal cognition at baseline
Bibliographic record
Abstract
Background and objective Cognitive impairment (CI, combing mild cognitive impairment and dementia) seriously affects the quality of life in patients with de novo Parkinson disease (PD). The aim of the present study was to identify the potential predictive factors for 5-year cognitive decline in de novo PD. Methods Parkinson’s Progression Marker Initiative (PPMI) database was retrieved and PD patients with normal global cognition at baseline were included. These patients were divided into normal cognitive (NC) group and CI group based on their Montreal Cognitive Assessment (MoCA) scores at the 5-year follow-up period. A total of 66 baseline variables were compared between these two groups. Univariate and multivariate logistic regression analyses were conducted, followed by the development and validation of a nomogram to predict 5-year global cognitive decline in de novo PD patients. Results A total of 344 PD patients with normal baseline cognition were included, in which 73 individuals developed CI at the 5 year follow-up period. Baseline MoCA, Benton Judgment of Line Orientation (BJOLO), Hopkins Verbal Learning Test (HVLT) immediate recall, Letter Number Sequencing (LNS), Symbol-Digit Modalities Test (SDMT), Semantic Fluency Test (SFT) scores, and Scale for Outcomes in Parkinson’s disease for Autonomic symptoms (SCOPA-AUT) total, gastrointestinal, and sexual dysfunction scores were selected out from the 66 potential predictors based on logistic regression analysis. These predictors were finally included in the nomogram of the model. The area under the ROC curve of the model was 0.870 (95% CI, 0.825–0.915). Conclusion Our study constructed a model that predicts 5-year cognitive decline in de novo PD with high accuracy, which may allow for the early risk stratification of future CI in PD patients at baseline.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".