Incremental Effects of Subsequent Concussions on Cognitive Symptoms in the Sport Concussion Assessment Tool
Bibliographic record
Abstract
OBJECTIVE: Patients who are fully recovered from a concussion may still be more vulnerable in the face of subsequent concussions. This study examines symptoms associated with repeated concussions in young and otherwise healthy adults. DESIGN: Cross sectional. SETTING: Institutional study at a university setting. PARTICIPANTS: University students with a history of concussion. INDEPENDENT VARIABLES: Participants were grouped based on numbers of concussions. MAIN OUTCOME MEASURES: The impact of incremental concussion on symptom clusters in Sport Concussion Assessment Tools 5 and Spearman ranking correlation coefficients between symptom clusters. RESULTS: One hundred thirty-five participants reported having had 1 concussion, 63 reported 2 concussions, 50 reported 3 concussions, and 43 reported 4 to 6 concussions. Total severity scores over the range of concussion number (1, 2, 3, and greater than 3) did not show a clear incremental effect. However, average scores of cognitive symptoms rose with each subsequent concussion ( P ≤ 0.05). The largest incremental effect observed was that of second concussions on emotional symptom scores (t = 5.85, P < 0.01). Symptoms in the emotional and cognitive clusters were the most correlated regardless of the number of reported concussions; the correlations were lowest with symptoms associated with sensitivity to light or noise. CONCLUSIONS: The incremental rise of cognitive symptom scores with each concussion affirms the importance of cognitive impairment in concussion assessment and implies a cumulative brain vulnerability that persists even after symptom resolution. The cognitive-emotional symptom clusters may reflect underlying concussion-induced impairments in the corticostriatothalamocortical (CSTC) networks, although sensitivity symptoms are potentially attributable to different neural correlates.
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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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".