Cognitive Testing and Exercise to Assess the Readiness to Return to Play After a Concussion
Bibliographic record
Abstract
ABSTRACT Introduction The decision to clear an athlete to return to play (RTP) after a concussion is critical given the potential consequences of premature RTP. Accordingly, this study aimed to investigate more sensitive ways to assess readiness for RTP. We examined postexercise cognitive assessment in recently concussed and asymptomatic university athletes who were cleared to RTP. Methods Forty recently concussed athletes and 40 control athletes without a history of concussion participated in the study. Athletes completed a switch task preexercise and postexercise (20 min on an ergometer at 80% maximal heart rate). A series of one-way ANOVA were performed to compare accuracy and response time between the concussion and the control groups on the switch task. Given that the clinical recovery of the participants in the concussion group could be heterogeneous, we also ran χ 2 tests to identify the presence of subgroups. Specifically, we aimed to determine whether a group difference existed in the proportion of concussed participants who underperformed. Results No difference between the concussion and control groups was observed for reaction time. However, a significant group difference was found for accuracy, with athletes from the concussion group exhibiting lower accuracy relative to the control group. Irrespective of condition (rest, postexercise), up to 30% of athletes from the concussion group were 2 SD lower when compared with the control group's average score. A third of the athletes only exhibited deficits after exercise. Conclusions Our results highlight the importance of considering interindividual differences in recovery trajectories. Although asymptomatic and cleared to RTP, an important portion of athletes had not completely recovered from their concussion. Fortunately, these athletes can be readily identified by using sensitive cognitive tests administered after a moderate-to-vigorous exercise.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".