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 traumatic brain injury (TBI) 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 TBI (mTBI), suggesting that tests of 3D multiple object tracking (3D-MOT) may be sensitive to post-concussion syndrome (PCS) 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 and 73 years. 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 PCS (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. Persons with a history of concussion in the past 37 days were predicted to have pPCS if they were ≥35 years of age, or if they were <35 years of age 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) | 0.000 | 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 | 0.000 |
| 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".