Evaluation of Optical Coherence Tomography Results and Cognitive Functions in Patients with Restless Legs Syndrome
Bibliographic record
Abstract
INTRODUCTION: The aim of this study was to evaluate whether retinal neural network was impaired and cognitive functions were disturbed in restless legs syndrome (RLS) considering the hypothesis that there may be a dysfunction in dopaminergic pathways in RLS like in Parkinson's disease. Therefore, we evaluated retinal neural network with optical coherence tomography (OCT) and presence of cognitive impairment with Montreal Cognitive Assessment (MOCA). METHODS: OCT evaluations were performed for 30 RLS patients and 30 healthy controls. Ganglion cell complex was segmented to retinal nerve fiber layer (RNFL), ganglion cell layer (GCL), and inner plexiform layer (IPL) automatically by the device, and recorded. Additionally, all the patients and the controls were evaluated using MOCA. RESULTS: No statistically significant difference was detected between RLS and controls in RNFL, GCL, IPL, and choroidal thicknesses. However, total MOCA score and all of its subscale scores were significantly lower in the RLS patients compared with the controls. No significant correlation was detected between OCT and MOCA parameters. CONCLUSION: No degeneration was detected in retinal neurons (RNFL, GCL, and IPL) of RLS patients. However, impairments were seen in MOCA total and subscale scores of these patients. On the other hand, no significant correlation was detected between MOCA scores and RNFL, GCL, or IPL thicknesses. These findings suggest decrease in cognitive functions of RLS patients probably due to dopaminergic dysfunction regardless of anatomical neural degeneration. Longitudinal follow-up studies are warranted to evaluate whether neuronal degeneration will develop.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".