Association of Preexisting Mental Health Conditions With Increased Initial Symptom Count and Severity Score on SCAT5 When Assessing Concussion
Bibliographic record
Abstract
Background: Mental health conditions, such as depression, anxiety, and learning disabilities, are associated with symptoms that can overlap with those seen in persons with concussion. While concussion screening tools such as the Sport Concussion Assessment Tool–5th Edition (SCAT5) quantify the number of symptoms and symptom severity, it is not known whether these outcomes differ among individuals with concurrent mental health conditions compared with those without them. Purpose: To determine whether, during initial concussion assessment, individuals with a self-reported mental health condition have a significantly different number of self-reported concussion symptoms or symptom severity compared with those without a self-reported mental health condition (controls). Study Design: Cohort study; Level of evidence, 3. Methods: A retrospective chart review was performed on consecutive patients aged ≥13 years who underwent post-concussion assessment at the Fowler Kennedy Sport Medicine Clinic between May 2018 and March 2020 (N = 765). Most participants did not self-report a mental health condition (n = 606; 79.2%). Participants with a self-reported mental health condition (n = 159) were classified as having a learning disability (n = 14; 8.8%), anxiety (n = 62; 39.0%), depression (n = 20; 12.6%), or multiple conditions (≥2 conditions: n = 63; 39.6%). Each participant with a mental health condition was matched with 2 control participants (overall pool, n = 318) based on age, sex, student status, and sport-related risk. Mann-Whitney U tests were used to determine the statistical significance of differences between each subgroup and their matched controls for the self-reported number of concussion symptoms and symptom severity as measured using the SCAT5. Results: The anxiety and multiple-conditions subgroups had a significantly greater number of reported symptoms than their corresponding control subgroups (median, 17 vs 15 [ P = .004] and 18 vs 14.5 [ P < .001], respectively). Additionally, the SCAT5 symptom severity score was significantly greater in the anxiety and multiple-conditions subgroups than their corresponding controls (median, 49 vs 34.5 [ P = .018] and 62 vs 32 [ P < .001], respectively). Conclusion: During initial concussion assessment, both the number of concussion-related symptoms and the symptom severity were greater in study participants with anxiety and multiple mental health conditions than participants without these conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".