Sports Related Concussion Impacts Speech Rate and Muscle Function
Bibliographic record
Abstract
ABSTRACT Objective To examine speech rate and muscle function in athletes with and without sports related concussion (SRC). Methods We recruited 30 athletes aged 19-22 years-old who had sustained a SRC within the past 2 years and 30 pair-wise matched controls with no history of SRC from the student community at Michigan State University. Speech rate and muscle function were evaluated during diadochokinetic (DDK) tasks. Speech rate was measured via average time per syllable, average unvoiced time per syllable, and expert perceptual judgement. Speech muscle function was measured via surface electromyography over the obicularis oris, masseter, and segmental triangle. Group differences were assessed using MANOVA, bootstrapping and predictive ROC analyses. Results Athletes with SRC had slower speech rates during DDK tasks than controls as evidenced by longer average time per syllable (F(1, 52) = 11.072, p =.002, [95% CI : .01 to .04]), longer average unvoiced time per syllable (F(1, 52) = 16.031, p < .000, [95% CI : .01 to .029] and expert judgement of slowed rate (F(1, 22) = 9.782, p = .005, [95% CI : .163 to .807]). Rate measures were predictive of concussion history. Further, athletes with SRC required more speech muscle activation than controls to complete the DDK tasks (F(1, 3) = 17.12, p =.000, [95% CI: .003 to .006]). Conclusion We found clear evidence of slowed speech and increased muscle activation during the completion of DDK tasks in athletes with SRC histories relative to controls. Speech rate and muscle assessment should be incorporated into clinical evaluation of concussion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".