An Integrated Review of Recovery after Mild Traumatic Brain Injury (MTBI): Implications for Clinical Management
Bibliographic record
Abstract
The diagnosis and treatment of mild traumatic brain injury (MTBI)have historically been hampered by an incomplete base of scientific evidence to guide clinicians. One question has been most elusive to clinicians and researchers alike: What is the true natural history of MTBI? Fortunately, the science of MTBI has advanced more in the last decade than in the previous 50 years, and now reaches a maturity point at which the science can drive an evidence-based approach to clinical management. In particular, technological advances in functional neuroimaging have created a powerful bridge between the clinical and basic science of MTBI in humans. Collectively, findings from clinical, basic science, and functional neuroimaging studies now establish a foundation on which to build integrative theories and testable hypotheses around a comprehensive model of MTBI recovery. We review the current scientific literature on postconcussion symptom recovery, neuropsychological outcome, and neurophysiological healing after MTBI. Special emphasis is placed on how the new evidence base can help guide clinicians in the evaluation and management of military-related MTBI.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".