Three-dimensional multiple object tracking in the pediatric population
Bibliographic record
Abstract
As mild traumatic brain injury (mTBI) affects hundreds of thousands of children and their families each year, investigation of potential mTBI assessments and treatments is an important research target. Three-dimensional multiple object tracking (3D-MOT), where an individual must allocate attention to moving objects within 3D space, is one potentially promising assessment and treatment tool. To date, no research has looked at 3D-MOT in a pediatric mTBI population. Thus, the aim of this study was to examine 3D-MOT learning in children and youth with and without mTBI. Thirty-four participants (mean age=14.69±2.46 years), with and without mTBI, underwent six visits of 3D-MOT. A two-way repeated-measures analysis of variance (ANOVA) showed a significant time effect, a nonsignificant group effect, and a nonsignificant group-by-time interaction on absolute speed thresholds. In contrast, significant group and time effects and a significant group-by-time interaction on normalized speed thresholds were found. Individuals with mTBI showed smaller training gains at visit 2 than healthy controls, but the groups did not differ on the remaining visits. Although youth can significantly improve their 3D-MOT performance following mTBI, similar to noninjured individuals, they show slower speed of processing in the first few training sessions. This preliminary work suggests that using a 3D-MOT paradigm to train visual perception after mTBI may be beneficial for both stimulating recovery and informing return to activity decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".