Multi-Disciplinary Management of Athletes with Post-Concussion Syndrome: An Evolving Pathophysiological Approach
Bibliographic record
Abstract
Historically, patients with sports-related concussion (SRC) have been managed in a uniform fashion consisting mostly of prescribed physical and cognitive rest with the expectation that all symptoms will spontaneously resolve with time. Although this approach will result in successful return to school and sports activities in the majority of athletes, an important proportion will develop persistent concussion symptoms characteristic of post-concussion syndrome (PCS). Recent advances in exercise science, neuroimaging, and clinical research suggest that the clinical manifestations of PCS are mediated by unique pathophysiological processes that can be identified by features of the clinical history and physical examination as well as the use of graded aerobic treadmill testing. Athletes who develop PCS represent a unique population whose care must be individualized and must incorporate a rehabilitative strategy that promotes enhanced recovery of concussion-related symptoms while preventing physical deconditioning. In this review, we present our evolving evidence-based approach to evaluation and management of athletes with PCS that aims to identify the pathophysiological mechanisms mediating persistent concussion symptoms and guides the initiation of individually tailored rehabilitation programs that target these processes. In addition, we outline the important qualified roles that multi-disciplinary healthcare professionals can play in the management of this patient population, and discuss where future research efforts must be focused to further evaluate this evolving pathophysiological approach.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".