Retinal thickness as a potential biomarker of neurodegeneration and a predictor of early cognitive impairment in patients with multiple sclerosis
Why this work is in the frame
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Bibliographic record
Abstract
OBJECTIVES: The purpose of this research is to predict the cognitive impairment and to determine its correlation with retinal thickness, mainly (RFNL and GCIPL) in cases of multiple sclerosis. METHODS: 60 multiple sclerosis patients and 30 age and sex-matched healthy controls were included in this study. Cognitive functions were evaluated in all study participants by the Montreal Cognitive Assessment (MoCA). OCT imaging was done to determine the thickness. The correlation between the cognitive domains of MoCA and the thickness of the retinal nerve fiber layers was analyzed by Spearman correlation. ROC curve was constructed to determine the cut-off points for retinal thickness, and a binary logistic regression was performed to determine the independent predictive capacity of established cut-off points. RESULTS: Impaired cognition was found in 26 MS patients (43.3%). Cognitively impaired patients were significantly older (P < 0.05), had significantly longer disease duration (P < 0.05), had higher average EDSS scores (4.3 ± 1.22 vs 3.1 ± 1.45, P < 0.001), and occurred more in progressive types of MS (P < 0.001). A significant positive correlation was found between cognitive function and RNFL thickness and GCIPL (P < 0.001). The retinal thickness (RNFL and GCIPL) cut-off points established for the prediction of cognitive impairment in MS patients were 79 μm and 76 μm, respectively. CONCLUSION: The clear correlation between cognitive impairment and atrophy of inner retinal layers (RNFL and GCIPL) proposes that OCT is valuable in evaluating the neurodegeneration and prediction of early cognitive impairment in MS. ABBREVIATIONS: EDSS: Expanded Disability Status Scale; HCs: Healthy controls; GCIPL: Ganglion cell-inner plexiform layer; ILM: Internal limiting membrane; INL: Inner nuclear layer; MoCA: Montreal Cognitive Assessment; MS: Multiple sclerosis; PPMS: Primary progressive multiple sclerosis; RNFL: Retinal nerve fiber layer; RRMS: Relapsing-remitting multiple sclerosis; SD: Standard deviations; SPMS: Secondary progressive multiple sclerosis; SPSS: Statistical Package for the Social Sciences.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it