P.034 Eye movement assessment and diffusion tensor imaging in patients with post concussion syndrome
Bibliographic record
Abstract
Background: Post concussion syndrome (PCS) can affect up to 30% of patients with concussion. Biomarkers of this condition would be beneficial for diagnosis and management. We hypothesized that eye tracking parameters would correlate with microstructural changes of white matter integrity, as measured by diffusion tensor imaging (DTI), in patients with PCS. Methods: Sixty patients with PCS and at least 2 concussions participated in our prospective study. Attention and executive function were tested using Visual Attention Scanning Technology (VAST). In a matching task, the normalized number of visits to master image before making the first selection is used as a surrogate of working memory. We related performance on VAST to white matter integrity using Tract-Based Spatial Statistics of diffusion tensor imaging data. Results: 60 participants (mean age 34.3 years, SD 13.8) had a mean of 4 concussions. There were negative correlations between fractional anisotropy (FA) of the genu, body, and splenium of the corpus callosum and normalized number of visits to master image before first selection (r=-0.432, p=0.001; r=-0.504, p<0.001; and r=-0.388, p=0.002; respectively). A negative correlation was also seen between right cingulum FA and the global processing index (r=-0.349, p=0.006). Conclusions: Impaired performance on eye tracking measures of attention and executive function may reflect alterations in white matter tracts.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".