Examining the cumulative effect of repetitive head-impacts on the ability to inhibit a motor response
Bibliographic record
Abstract
Objective To examine how a season of sub-concussive head-impact exposures influence a measure of response-inhibition in contact-sport athletes. Design Prospective cohort Setting Laboratory Participants Twenty-five contact-sport athletes (20.3±1.3 years) performed a response-inhibition task prior to and following the competitive season. Each player wore an adhesive accelerometer (X2Biosystems) during games. Players were excluded if they sustained a concussion during the season. Interventions To probe response-inhibition, an object hit-and-avoid sensorimotor task (KINARM) was performed. This 2.5 minute task entailed the successful hitting of two target shapes while avoiding six other distractor shapes. Players were divided into tertiles (high, medium, low; n=8) based on cumulative number of 20g+ head impacts, cumulative peak linear acceleration (cPLA), and cumulative peak rotational acceleration (cPRA). Comparisons of response-inhibition performance were made between high and low tertiles. Outcome measures Pre-post changes in number of targets hit, distractors hit, total cancelled movements towards pursued distractors (CPDs), and total task accuracy (percent) were recorded for the KINARM task. Results Independent t-tests indicated no significant differences between the high and low tertiles on the KINARM task despite the high tertile group experiencing 4-times greater head impacts throughout the season. There was a trend for greater KINARM task improvement from pre-to-post-season in the bottom tertile, as reflected by the increase in CPDs in their post-season measures (cumulative number: p=0.06, CI=[−16.2148519, 0.7148519]). Conclusions These findings reveal that one season of repetitive sport-related head impacts does not appear to have a significant effect on an athlete’s ability to inhibit a motor response. Competing interests All authors have no competing interests to declare.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".