Perceptual Cognitive Training Does Not Improve Clinical Outcomes at 4 and 12 Weeks Following Concussion in Children and Adolescents: A Randomized Controlled Trial
Bibliographic record
Abstract
OBJECTIVE: To determine whether a perceptual-cognitive training program using 3D-multiple object tracking (3D-MOT) can improve symptoms following acute pediatric concussion. SETTING: Research laboratory within a pediatric trauma center. PARTICIPANTS: Children and adolescents (n = 62, age= 13.27 ± 2.50) with diagnosed concussion. DESIGN: Randomized controlled trial. Children were randomized into either 3D-MOT, 2048 game, or standard care-only groups. Participants and parents completed the Post-Concussion Symptom Inventory (PCSI) at baseline, 4, 8, and 12 weeks postinjury. Intervention participants completed either the 3D-MOT protocol or the 2048 game at 6 sessions between the baseline and 4-week assessment. MAIN MEASURES: A 3 (group) × 10 (time) mixed-model analysis of variance evaluated PCSI total scores. The rate of persistent postconcussive symptom (PPCS) was evaluated at 4 weeks using χ2 analysis. RESULTS: Symptoms decreased throughout the study using both child-reported (F(9,374) = 22.03, P < .001) and parent-reported scores (F(9,370) = 28.06, P < .001). Twenty-four (44.4%) children met the study definition for PPCS using the child-reported PCSI, while 20 (37.7%) children had PPCS using parent reports. The intervention did not significantly affect symptom resolution or PPCS rates. CONCLUSION: There is no benefit to prescribing 3D-MOT training for acute rehabilitation in pediatric patients with concussion and clinicians should instead focus on more effective programs.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".