Working memory load improves diagnostic performance of smooth pursuit eye movement in mild traumatic brain injury patients with protracted recovery
Bibliographic record
Abstract
Smooth pursuit eye movements have been investigated as a diagnostic tool for mild traumatic brain injury (mTBI). However, the degree to which smooth pursuit differentiates mTBI patients from healthy controls (i.e. its diagnostic performance) is only moderate. Our goal was to establish if simultaneous performance of smooth pursuit and a working memory task increased the diagnostic performance of pursuit metrics following mTBI. We integrated an n-back task with two levels of working memory load into a pursuit target, and tested single- and dual-task pursuit in mTBI patients and healthy controls. We assessed pursuit using measures of velocity accuracy, positional accuracy and positional variability. The mTBI group had higher pursuit variability than the control group in all conditions. Performing a concurrent 1-back task decreased pursuit variability for both the mTBI and control groups. Performing a concurrent 2-back task produced differential effects between the groups: Pursuit variability was significantly decreased in the control group, but not in the mTBI group. Diagnostic indices were improved when pursuit was combined with the 2-back task, and increased by 20% for the most sensitive variable. Smooth pursuit with simultaneous working memory load may be a superior diagnostic tool for mTBI than measuring smooth pursuit alone.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".