The impact of concussion on brain adaptation: the use of prism glasses as a novel diagnostic tool
Bibliographic record
Abstract
Objective To evaluate brain adaptation following concussion. Do youth with a history of previous or acute concussion require more ball throws to land on or near a target, when wearing prism glasses (bend light to the left by 15˚), than youth with none? Design Cross-sectional study. Setting Sport Injury Prevention Research Centre, Alberta, Canada. Participants Ice hockey players: 21 with acute concussion [(17 males), mean age 13.8 (95% CI: 13.4, 14.2), median number of days since concussion 4 (range 2–11)], 40 reporting previous concussion(s) [(33 males), mean age 13.4 (95% CI: 13.1, 13.7), median number of days since last concussion 620 (range 90–1560)], and 40 with no concussion history [(36 males), mean age 12.9 (95% CI: 12.5, 13.2)]. Independent variable Lifetime concussion history (yes/no) or acute concussion (<10 days). Dependent variable Number of throws for brain adaptation. Main results A significant concussion effect was identified across groups [F(2,98)=26.17, p<0.001]. Post-hoc analyses showed a difference in the number of throws in players with no concussion history versus players reporting previous concussions [−4.700 (95% CI: −8.44, −0.96)] and acute concussions [−13.440 (95% CI: −17.89, −8.99)], as well as between previous history versus acute concussion groups [−8.740 (95% CI: −13.19, −4.29)]. Conclusions Concussion history and acute concussion appears to negatively impact brain plasticity in youth. They do not adapt as well as individuals with no concussion history. Our results support the use of a prism adaptation paradigm for identifying/quantifying short and long-term neurologic impairments in youth following concussion. Competing interests None.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".