Capturing Subjective Cognitive Decline with a new combined index in low education patients with Parkinson’s Disease
Bibliographic record
Abstract
Abstract Subjective Cognitive Decline (SCD), refers to self-reported cognitive decline with normal global cognition. We aimed to capture SCD among low educated patients with Parkinson’s disease (PD) by a newly established indicator. There were total of 64 PD patients with low education levels (education ≤ 12 years) recruited in this study. The presence of SCD was determined based on a Unified Parkinson’s Disease Rating Scale Part I (1.1) score ≥ 1. The prevalence of SCD in PD patients was 43.75% (28/64). Low educated PD-SCD patients had higher scores on the Non-Motor Symptoms Scale (NMSS), Parkinson’s Fatigue Scale (PFS), Epworth Sleepiness Scale (ESS), as well as higher scores on the UPDRS-I and UPDRS-II, compared to PD patients without SCD. Multivariate binary regression confirmed the significant association between PD-SCD and MoCA-executive abilities/attention/language. A score of 12 or less on the combined index had a sensitivity of 73.9% and a specificity of 76.2% for diagnosing PD-SCD. Most importantly, the newly combined index can help capture these low educated PD-SCD patients, with an AUC of 0.867, and is expected to assist clinicians in earlier identification of PD patients.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 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 | 0.001 |
| 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".