Videonystagmography features and clinical symptoms correlate with Parkinson's clinical subtypes
Bibliographic record
Abstract
Objective: Analyzing the differences in video-nystagmography (VNG) parameters among Parkinson's disease (PD) patients with distinct motor subtypes and evaluate the correlation between VNG parameters and the severity of non-motor symptoms (NMS) in Tremer Dominant (TD)/Postural Instability/Gait Difficulty (PIGD) subtypes provides a theoretical basis for advancing the understanding of PD heterogeneity. Methods: Sixty-nine patients with PD diagnosed in the Department of Neurology of the First Affiliated Hospital of Hainan Medical College from December 2022 to January 2024 were collected. Among them, 35 were in the Tremer Dominant (TD) group, 34 were in the Postural Instability/Gait Difficulty (PIGD) group, and 38 patients with physical examination admitted in the same period were collected as the healthy control (HC) group. The subjects were assessed for motor and non-motor symptoms, and the VNG was refined to record the assessment of eye movement abnormalities in the patients. The general clinical data and VNG parameters of the subjects in the three groups were analyzed and correlation analyses were performed for clinical features and nystagmus views with intergroup differences in the PIGD and TD groups. Results: > 0.05). Conclusion: VNG shows significant differences among Parkinson's disease patients with different motor subtypes, and VNG features are correlated with non-motor symptoms.
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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 | 0.000 |
| 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".