Sensitivity to sounds in sport-related concussed athletes: a new clinical presentation of hyperacusis
Bibliographic record
Abstract
Sensitivity to sounds is one frequent symptom of a sport-related concussion, but its assessment rarely goes beyond a single question. Here we examined sensitivity to sounds using psychoacoustic and psychometric outcomes in athletes beyond the acute phase of injury. Fifty-eight college athletes with normal hearing who either had incurred one or more sport-related concussions (N = 28) or who had never suffered head injury (N = 30) participated. Results indicated that the Concussed group scored higher on the Hyperacusis questionnaire and displayed greater sensitivity to sounds in psychoacoustic tasks compared to the Control group. However, further analyses that separated the Concussed group in subgroups with Sound sensitivity symptom (N = 14) and Without sound sensitivity symptom (N = 14) revealed that athletes with the sound complaint were the ones responsible for the effect: Concussed athletes with self-reported sound sensitivity had lower Loudness Discomfort Thresholds (LDLs), higher Depression and Hyperacusis scores, and shifted loudness growth functions compared to the other subgroup. A simple mediation model disclosed that LDLs exert their influence both directly on Hyperacusis scores as well as indirectly via depressive symptoms. We thus report a new clinical presentation of hyperacusis and discuss possible mechanisms by which it could arise from concussion.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".