Sensitivity and Specificity of Exercise Intolerance on Graded Exertion Testing for Diagnosing Sport-Related Concussion: A Systematic Review and Exploratory Meta-Analysis
Bibliographic record
Abstract
There is no single gold standard test to diagnose sport-related concussion (SRC). Concussion-related exercise intolerance, that is, inability to exercise to the individual's appropriate level due to exacerbation of concussion-like symptoms, is a frequent finding in athletes early after SRC that has not been systematically evaluated as a diagnostic test of SRC. We performed a systematic review and proportional meta-analysis of studies that evaluated graded exertion testing in athletes after SRC. We also included studies of exertion testing in healthy athletic participants without SRC to assess specificity. Pubmed and Embase were searched in January 2022 for articles published since 2000. Eligible studies included those that performed graded exercise tolerance tests in symptomatic concussed participants (> 90% of subjects had an SRC, seen within 14 days of injury), at the time of clinical recovery from SRC, in healthy athletes, or both. Study quality was assessed using the Newcastle-Ottawa Scale. Twelve articles met inclusion criteria, most of which were of poor methodological quality. The pooled estimate of incidence of exercise intolerance in participants with SRC equated to an estimated sensitivity of 94.4% (95% confidence interval [CI]: 90.8, 97.2). The pooled estimate of incidence of exercise intolerance in participants without SRC equated to an estimated specificity of 94.6% (95% CI: 91.1, 97.3). The results suggest that exercise intolerance measured on systematic testing within 2 weeks of SRC may have excellent sensitivity for helping to rule in the diagnosis of SRC and excellent specificity for helping to rule out SRC. A prospective validation study to determine the sensitivity and specificity of exercise intolerance on graded exertion testing for diagnosing SRC after head injury as the source of symptoms is warranted.
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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 | 0.024 | 0.064 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.053 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".