Saccadic Eye Movements: Giving us a Glimpse into understanding Cognitive Impairment
Bibliographic record
Abstract
Background Multiple sclerosis (MS) frequently causes cognitive impairment (CI), with learning, attention and memory being the most frequently impaired. The Symbol Digit Modalities Test (SDMT) measures visual scanning and information processing speed and accounts for the highest portion of the variance when classifying people with MS (PwMS) and healthy controls (HC). The Montreal Cognitive Assessment (MoCA) is a widely used screening assessment for detecting cognitive impairment. PwMS have been shown to have abnormally slow saccadic eye movements. The King‐Devick (K‐D) Test, a test of attention and rapid eye movements, is used in the field to detect concussions among athletes. We want to determine the feasibility of administering the K‐D test in a routine clinical visit of PwMS. Hypothesis We believe the K‐D test can be used to help detect CI in PwMS. Methods We recruited PwMS and HC from the LSU MS Clinic and the community. Participants completed the K‐D Test, SDMT, and the MoCA. All HC scoring ≤25 on the MoCA were excluded. PwMS scoring ≤25 on the MoCA were categorized as CI. We determined the correlations between each of the tests using a Pearson correlation. Results We recruited 38 PwMS (84% females, mean age 48±13 years; years of education 15±2.8; 47% CI) and 18 HC (61% females, age 56±12 years; education 15±2.8). The HC scored significantly better than the PwMS on the SDMT (60±11.3 and 52.3±9.1, p=0.01) and MoCA (28±1.2 and 26±2.4, p<0.01). In the PwMS CI group, the K‐D correlated with SDMT (r= −0.43, p=0.06) and MoCA (r= −0.59, p=0.006); in the PwMS not impaired group, the K‐D correlated with SDMT (r= −0.17, p=.48); and MoCA (r= −0.16, p=.51). In HC, the K‐D correlated with SDMT (r= −0.59, p=0.01) and MoCA (r= −0.21, p=.41). Conclusion The K‐D test may detect CI in PwMS since there appears to be a link between saccadic eye movements and cognitive impairment. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".