Value of saccadic latency as a diagnostic tool for multiple sclerosis
Bibliographic record
Abstract
Background Multiple sclerosis (MS) is a chronic and progressive autoimmune disorder that affects the Central Nervous System (CNS). MS is a clinical diagnosis that is confirmed by MRI, visual evoked potentials, and CSF examination. The objective of the current review and meta-analysis is to evaluate the value of saccadic eye movement abnormalities – particularly saccadic latency – as a diagnostic tool for Multiple Sclerosis. Methods We searched the literature in MEDLINE, PubMed, Web of Science and Cochrane Library from 1 st January 1998, through 1 st September 2018. Published studies of adult patients who are diagnosed with MS and had VOR testing including saccadic eye movement test, and reporting saccadic latency. We calculated pooled mean differences (MD) and 95% confidence intervals (CI) using a random-effects model for latencies in milliseconds (ms) between MS patients and normal controls. The considerable heterogeneity decided the effect model. Results Five studies m et al l inclusion criteria. MS patients had a significantly longer saccadic latency compared to the control group, with 135 MS cases and 126 controls, were included in this meta-analysis. The results indicated that there is a significant increase in saccadic latency in the MS group (MD = 31.99, 95% CI = 14.08, 49.90, p = .0005). Conclusion Based on current evidence from published studies, Saccadic latency can serve as a diagnostic tool to support the clinical diagnosis of MS.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.018 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".