Recovery from Mild Traumatic Brain Injury in the Nonathletic Population: A Systematic Review
Bibliographic record
Abstract
The objective of this study was to document the resolution rate of mild Traumatic Brain Injury (mTBI) symptoms at various time points in a nonathletic adult population and identify prognostic factors influencing recovery. Sixteen prospective cohort studies were included, focusing on participants aged 18-65 with acute mTBI, followed for a minimum of 1 month. The recovery criterion was the resolution of symptoms not attributable to pre-existing conditions. Risk of bias was assessed using the Quality in Prognostic Studies tool, with most studies rated as moderate risk, highlighting variability in methodological rigor. Symptom resolution was reported in 49.0% to 69.5% of patients at 1 month, 40.8% to 84.4% at 3 months, 38.3% to 72.2% at 6 months, and 58.1 to 68.3% at 12 months. These findings emphasize the first 6 months as a critical period for evaluating the risk of symptom chronicity. The most commonly reported prognostic factors was baseline symptom severity, including higher intensity of symptoms such as headaches, nausea, and dizziness, as well as elevated scores on validated symptom scales. Psychiatric history, such as pre-existing anxiety or depression, was also a significant predictor of prolonged symptoms. Biomarkers, including NSE and S-100B levels, and reduced blood-derived neurotrophic factors, were associated with poorer recovery at 6 months. Demographic factors, including age, gender, and education level, showed mixed results. While some studies associated female gender, older age, and lower education with poorer recovery, others found no significant correlations. These discrepancies highlight the complexity of mTBI prognosis. Overall, more than half of patients recover within 6 months, but persistent symptoms can have a profound impact on quality of life and functional status. Identifying patients at higher risk of prolonged recovery is crucial for targeted management strategies, emphasizing the importance of individualized, evidence-based care in mTBI populations.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".