Sensitivity and Specificty of a Multimodal Approach for Concussion Assessment in Youth Athletes
Bibliographic record
Abstract
Background: Concussions are a heterogeneous and complex injury that may negatively impact the lives of youth. Multimodal assessment is recommended, however, validation of concussion measures in youth athlete populations remains limited. Purpose: To explore the sensitivity and specificity of a multimodal approach compared to individual clinical measures for the assessment of concussion in youth athletes at symptomatic and asymptomatic time points following concussion. Methods: A prospective, longitudinal cohort study with matched non-injured controls was used. Youth athletes (10-18 years) were assessed using a multimodal approach consisting of cognitive, balance, strength and symptom measures. Results: Cognitive, balance, and strength measures were sensitive and specific to declines in performance following a concussion. Multimodal assessment can more accurately distinguish between concussed and non-injured controls compared to stand-alone measures when post-concussion symptoms resolve. Conclusions: Findings may help guide clinical decision making and contribute to future research exploring approaches to concussion assessment in youth athletes.
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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 | 0.014 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".