Exploring changes in processing speed and intraindividual variability in youth following sports-related concussion
Bibliographic record
Abstract
Background Concussion represents a growing concern in sports participation for adults and youth alike. Studies exploring the neurocognitive sequelae of concussion, such as speed of processing typically compare mean reaction time scores to a control group. Intraindividual variability measures the consistency of reaction times between trials and has been previously explored in adults post-concussion. Some adult studies show increased variability following injury. Developmentally youth show higher intraindividual variability than adults, which may put them at higher risk of increased intraindividual variability change post-concussion. Exploring intraindividual variability may provide additional insight into fluctuating performance reported following injury. Despite preliminary findings of slowed reaction time in youth, a pre-/post-concussion comparison of intraindividual variability of reaction time has not been explored. Objective To describe and compare pre- and post-concussion measures of processing speed and intraindividual variability in youth. Methods A pre-/post-concussion design was used to compare mean reaction time and the coefficient of variation before and after sports-related concussion in 18 youth athletes aged 10–14 years using verbal and nonverbal working memory tasks. Pre-/post-concussion reaction time and coefficient of variation were compared using t-tests. Results The coefficient of variation for nonverbal working memory was significantly higher following concussion, but no changes in average reaction time were found. Conclusions Preliminary findings suggest that average response times are unchanged following concussion, but the fluctuation across response times is more variable during a nonverbal working memory task in youth. Increased variability in speed of reaction times could have implications for safe return to sports and reduced academic performance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".