Eye movement and white matter integrity in patients with post-concussion syndrome
Bibliographic record
Abstract
Objective To assess eye movements in patients with post-concussion syndrome (PCS) and use diffusion tensor imaging (DTI) to determine relationship between eye movements and white matter integrity. Design cross-sectional study. Intervention Attention and executive function were tested using Visual Attention Scanning Technology (VAST) [EL-MAR Inc., Toronto, Ontario, Canada]. In a matching task, the normalised number of transitions (NNT) to a master image before making the first selection was used as a surrogate of working memory. During this task, subjects view a set of slides, each slide includes a master image and six variants, and they have to select a variant that is similar to the master (only one variant is an exact replica of the master). Outcome measures We related performance on VAST to white matter integrity using Tract-Based Spatial Statistics of DTI metrics such as fractional anisotropy (FA) for whole brain analyses as well as seed-based probabilistic tractography analysis of the superior longitudinal fasciculus (SLF), Cingulum tract, and Uncinate fasciculus (UF). Main results 60 participants (mean age 34.3 years, SD 13.8) had a mean of 4 concussions. There were negative correlations between whole brain FA and NNT (r=−0.501, p<0.001). In addition, when we performed probabilistic tractography analyses, we found a negative correlation between the FA of right SLF and NNT (r=−0.332, p=0.009). Conclusions Impaired performance on eye tracking measures of attention and executive function may reflect alterations in white matter tracts. Competing interests None. Moshe Eizenman is a director in EL-MAR Inc.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".