Multiple object tracking scores predict post-concussion status years after mild traumatic brain injury
Bibliographic record
Abstract
The diagnosis of concussion remains challenging, particularly in cases where several months have passed between a head injury and clinical assessment. Tracking multiple moving objects in three-dimensional (3D) space engages many of the same cognitive processes that are affected by concussion, a form of mild traumatic brain injury (mTBI), suggesting that tests of 3D multiple object tracking (3D-MOT) may be sensitive to post-concussion syndrome after a brain injury has occurred. To test this, we evaluated 3D-MOT performance (using NeuroTrackerTM) against Sports Concussion Assessment Tool results for cognition, balance, and symptom severity in a large sample (N = 457) of male and female participants between the ages of 6 to 73. 3D-MOT performance in subjects under age 13 was not impaired by a history of concussion, but was positively associated with cognition and balance. 3D-MOT performance in those 13 and older was negatively associated with concussion symptom severity, and positively associated with cognition and balance. 3D-MOT was selectively impaired in subjects with probable post-concussion syndrome (pPCS), defined using the 95th percentile of symptom severity for subjects with no history of concussion. A decision tree predicted concussion status with 95.2% overall test accuracy (91.1% sensitivity, 97.8% specificity) using concussion history, age, and 3D-MOT score. Individuals with a history of concussion in the past 37 days were predicted to have pPCS if they were age 35 or older, or if they were under age 35 but achieved scores below 1.2 on the 3D-MOT. These results demonstrate the potential of 3D-MOT for pPCS diagnosis, and highlight the increased vulnerability to concussion symptoms that comes with age.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".