Clinically Historical and Prospective Associations Between Learning Disorders and Concussion in Young Adult Athletes
Bibliographic record
Abstract
Background. Athletes with specific learning disorder (LD) tend to score lower on neuropsychological tests and are at increased risk of personal injury than their counterparts without such disorders. Using a retrospective historical and prospective design, we examined whether adult athletes with LD, the most prevalent of neurodevelopmental disorders, experience greater chances of past and future concussions than their counterparts without LD. We expected to find that young athletes with LD would show greater risk of past (historical) and future (prospective) cerebral concussions. Methods. Participants (95 men and 53 women aged 18 to 25 years) were recruited from university sports teams and followed during an entire season. Of these, 38 participants had a history of LD and 101 had a history of at least 1 concussion (72 males, 29 females) at the preseason baseline. One-third experienced a new concussion. Data analytic procedures include inferential cross-tabulations. Results. Athletes with LD were twice more likely to have a concussion history at baseline and to have a history of multiple concussions than athletes without LD; 95% CI = [0.86, 4.92] and [0.77, 3.40], respectively. Athletes with LD were twice more likely to incur a new concussion than those without LD; 95% CI = [0.86, 4.92]. Conclusions. Adult athletes with LD experience greater chances of previous and future concussions compared with counterparts without LD. Preventive practices regarding individuals with neurodevelopmental disorders may not only prevent the biopsychosocial consequences of brain trauma for the individual, but also represent a cost-effective public health measure.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".