Concurrent And Prospective Associations Between Learning Disabilities And Concussion In Young Adults
Bibliographic record
Abstract
Concussive injuries are an increasing public health concern. Although considerable research efforts are dedicated towards understanding injury outcomes, less effort is devoted to understanding the risk factors. PURPOSE: To retrospectively and prospectively evaluate the relation between neurodevelopmental disorders and the risk of incurring concussive injuries. METHODS: 148 University athletes completed baseline testing, which included the assessment of learning disabilities and sport-related concussion. Odds ratios were calculated ((pa/1-pa)/(pb/1-pb)) for the 148 athletes who completed the baseline assessment and for the 48 athletes who incurred a concussion during the study to assess the relation of learning disabilities and the incidence of concussion. RESULTS: At baseline, 32 athletes had a history of one concussion and 59 athletes had a history of two or more concussions. Athletes with a learning disability were 2.06 times more likely to have a have a history of concussion and 1.63 times more likely to have a history of multiple concussions than those without a learning disability. Further, athletes with a learning disability were 2.62 times more likely to suffer a concussion during the course of the study than athletes without a learning disability.. CONCLUSION: The current data suggest that having a learning disability may be a significant risk factor for incurring a concussive injury.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".