Prodromal Association of Non-Motor Symptomatology in Adults With Neurodegenerative Diseases
Bibliographic record
Abstract
ABSTRACT Background: Early diagnosis of neurodegenerative diseases (NDs) is a priority with the advent of disease-modifying therapies. The study of prodromal non-motor symptoms (NMS) in ND could help guide early diagnosis, facilitating adequate treatment. The primary objective of this study was to determine the prodromal association between NMS and ND. Methods: A retrospective cohort study was planned. Records of subjects attending the movement disorders and NDs unit from Hospital Civil de Guadalajara “Fray Antonio Alcalde,” from 2012 to January 2025, were evaluated. Subjects with NMSQuest questionnaire in ND were included. Descriptive and inferential parametric statistics (Student’s t -test for independent samples and ANOVA) were used, and odds ratios (OD) with 95% confidence interval were calculated using binary logistic regression. A p value < 0.05 was considered significant. Results: The most frequent prodromal NMS in ND were depression, constipation, insomnia, anxiety and pain. There was an average of 7.7 ± 7.6 years of prodromal NMS prior to the diagnosis of ND. Prodromal depression [OR 1.9 (1.1–3.5 p < 0.001) and confusion/delusional thoughts [OR 7.2 (1.3–39.2) p = 0.02] were associated with dementia (time onset prior to diagnosis 9.9 ± 12.2 and 5.5 ± 9.7 years, respectively). Prodromal restless legs syndrome was associated with movement disorders [OR 3.3 (1.1–9.7) p = 0.02] (time onset 4.7 ± 6.9 years prior to diagnosis). Conclusion: Evidence points to a potential association of prodromal NMS in ND. Prospective studies are required to confirm and characterize the association between prodromal NMS and ND, as their early identification may contribute to the timely diagnosis of ND.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".