Capturing subjective cognitive decline with a new combined index in low education patients with Parkinson’s disease
Bibliographic record
Abstract
Objectives: Subjective Cognitive Decline (SCD) refers to self-reported cognitive decline with normal global cognition. This study aimed to capture SCD among low educated patients with Parkinson's disease (PD) using a newly established indicator. Methods: We recruited 64 PD patients with low education levels (education ≤12 years) for the study. The presence of SCD was determined based on a Unified Parkinson's Disease Rating Scale Part I (1.1) score ≥ 1. Spearman analysis and multivariate binary logistic regression analyses were conducted to investigate factors associated with the PD-SCD group. The receiver operating characteristic (ROC) curve was used to evaluate the sensitivity and specificity of the new combined index. Results: The prevalence of SCD in PD patients was 43.75%. 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. They also demonstrated poorer performance on the Montreal Cognitive Assessment (MoCA), particularly in the domains of executive abilities/attention/language. Multivariate binary regression confirmed the significant association between PD-SCD and MoCA-executive abilities/attention/language. Based on these findings, a combined index was established by summing the scores of MoCA-executive abilities, MoCA-attention, and MoCA-language. ROC analysis showed that the combined index could differentiate PD-SCD patients with an area under the curve (AUC) of 0.876. 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. Conclusion: These low education patients with PD-SCD may exhibit potential PD-related pathological changes. It is important for clinicians to identify PD-SCD patients as early as possible. 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 and better management of 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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| 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) | 0.001 | 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".