A single Center study of the Symbol Digit Modalities test as a screening tool for cognitive impairment in Parkinson’s disease
Bibliographic record
Abstract
Background: Parkinson's Disease (PD) can include physical signs and possibly cognitive impairment, resulting from the convergence of pathological processes involving dopaminergic dysfunction, accumulation of alpha-synuclein, cholinergic deficits, and disruption of other neurotransmitter systems. We used screening tests to evaluate the characteristics of cognitive performance in patients with PD and to assess their validity compared to the Montreal Cognitive Assessment (MoCA). Methods: This is a natural history study of participants with PD and controls screened for possible cognitive impairment using the MoCA, Symbol Digit Modalities Test (SDMT), and King-Devick (KD). The groups were compared on performance and then factors associated with cognitive performance (age, diagnosis, and level of education) were analyzed to determine which best predicted test scores. Results: SDMT scores were lower in the PD group (Mean = 36.7 ± 12.4) compared to controls (Mean = 47.2 ± 11.0, p < 0.001), but the MoCA (PD = 23.8 ± 3.5; Control = 25.5 ± 3.6, p = 0.02) and KD (PD = 70.1 ± 23.4 s; Control = 61.6 ± 17.5, p = 0.048) did not differentiate between groups after controlling for multiple comparisons. Age and diagnosis predicted SDMT raw scores and, as expected, only diagnosis remained significant after calculating T-scores based on published test norms. Age, education, and diagnosis predicted MoCA scores. Conclusions: The SDMT emerged as a promising screening tool to detect cognitive impairment in PD. The test's age and education corrected norms controlled for those variables and left diagnosis as the only predictor of performance. The MoCA scores were predicted by age, education, and diagnosis suggesting the education correction of the MoCA did not fully account for the influence of demographic variables.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".