Mental Health Outcomes Across the Lifespan in Individuals With Persistent Post-Concussion Symptoms: A Scoping Review
Bibliographic record
Abstract
Objective: Concussion is a common yet heterogenous injury. Approximately 15–30% of cases present with persistent post-concussion symptoms (PPCS), continuing 4 weeks or more post-injury in children, youth, and adolescents, and 3 months or more in adults. There are known bidirectional links between PPCS and mental health outcomes. The focus of this scoping review is to explore the literature on mental health outcomes in individuals experiencing PPCS. Research objectives were to explore: (1) the mental health outcomes of individuals with PPCS and types of assessments used to identify mental health outcomes this group, and (2) how mental health outcomes compare in terms of similarities and differences among pediatric and adult populations with PPCS. Method Ovid MEDLINE; EMBASE; CINAHL, and PsycInfo databases were searched. After title and abstract screening of 11,920 studies, 481 articles were reviewed. Twenty-five papers met inclusion criteria. Results were organized by mental health outcomes of pediatric and adult populations, separately. Results There was a significantly higher number of studies devoted to adult populations. Of the 25 studies, 19 (76%) focused on adults, while six (24%) focused on adolescents. In adult populations, studies focused on symptoms of: anxiety (n= 2), depression (n= 8), and anxiety and depression (n= 9). Two studies assessed other emotional outcomes (10.5%). Within pediatric populations, an equal number of studies explored symptoms of: anxiety (n= 2), depression (n= 2), and anxiety and depression (n= 2). No studies focused on other emotional outcomes. Studies ranged greatly in methods, design, and control group. Most studies reported higher psychiatric symptoms of anxiety and/or depression in those with PPCS compared to individuals with recovered concussion or healthy controls. Discussion This review contributes to the understanding of mental health outcomes in those experiencing PPCS. Mental health and PPCS requires greater attention in pediatric populations, and consider strategies for those experiencing PPCS and mental health impacts. Future studies should consider including a wider range of emotional outcomes in their design, not limited to anxiety and depression. Study results may lead to improvements and research in the identification, assessment, and management of PPCS and mental health.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".