Multiple Past Concussions in High School Football Players (P3.324)
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to determine if there are measureable differences in cognitive functioning or symptom reporting in high school football players with a history of multiple concussions. BACKGROUD: There is increasing concern about the possible long-term effects of multiple concussions, particularly on the developing adolescent brain. Whether or not the effect of multiple concussions is detectable in high school football players has not been well-studied, though the public health implications are great in this population. DESIGN/METHODS: Participants included 5,232 male adolescent football players (mean age=15.2 years, SD=1.2) who completed baseline testing between 2009 and 2014. Injury history was self-reported, allowing athletes to be grouped into zero (n=4,183), one (n=733), two (n=216), three (n=67), or four or more (n=33) prior concussions. Cognitive functioning measured by ImPACT® and symptom ratings from the Post-Concussion Scale. RESULTS: There were no statistically significant differences between groups (based on number of reported concussions) regarding cognitive functioning. Athletes with three or more prior concussions reported more symptoms than athletes with zero or one prior injury. In multivariate analyses, concussion history was independently related to symptom reporting, but less so than developmental problems (e.g., attention or learning problems) or other health problems (e.g., past treatment for psychiatric problems, headaches, or migraines). CONCLUSIONS: In the largest study to date, high school football players with multiple past concussions performed the same on cognitive testing as those with no prior concussions. Concussion history was one of several factors that were independently related to symptom reporting.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".