Post-Concussion Symptoms and Disability in Adults With Mild Traumatic Brain Injury: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Studies investigating long-term symptoms and disability after mild traumatic brain injury (mTBI) have yielded mixed results. This systematic review and meta-analysis aimed to determine the prevalence of self-reported post-concussion symptoms (PCS) and disability following mTBI. We systematically searched MEDLINE, Embase, CINAHL, CENTRAL, and PsycInfo to identify inception cohort studies of adults with mTBI. Paired reviewers independently extracted data and assessed risk of bias with the Scottish Intercollegiate Guidelines Network criteria. We identified 43 eligible studies for the systematic review; 41 were rated as high risk of bias, primarily due to high attrition (> 20%). Twenty-one studies (49%) were included in the meta-analyses (five studies were narratively synthesized; 17 studies were duplicate reports). At 3-6 months post-injury, the estimated prevalence of PCS from random-effects meta-analyses was 31.3% (95% confidence interval [CI] = 25.4-38.4) using a lenient definition of PCS (2-4 mild severity PCS) and 18.3% (95% CI = 13.6-24.0) using a more stringent definition. The estimated prevalence of disability was 54.0% (95% CI = 49.4-58.6) and 29.6% (95% CI = 27.8-31.5) when defined as Glasgow Outcome Scale-Extended <8 and <7, respectively. The prevalence of symptoms similar to PCS was higher in adults with mTBI versus orthopedic injury (prevalence ratio = 1.57, 95% CI = 1.22-2.02). In a meta-regression, attrition rate was the only study-related factor significantly associated with higher estimated prevalence of PCS. Setting attrition to 0%, the estimated prevalence of PCS (lenient definition) was 16.1%. We conclude that nearly one in three adults who present to an emergency department or trauma center with mTBI report at least mild severity PCS 3-6 months later, but controlling for attrition bias, the true prevalence may be one in six. Studies with representative samples and high retention rates are needed.
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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 | 0.018 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.042 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 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".