Military- and Sports-Related Mild Traumatic Brain Injury: Clinical Presentation, Management, and Long-Term Consequences
Bibliographic record
Abstract
Article Abstract Click to enlarge page Awareness that concussions are more serious than previously believed has been increasing. Also known as mild traumatic brain injury (mTBI), concussions often occur, and often multiple times, in both military and sports settings. Brain injuries can seriously and negatively impact patients, leading to changes in personality, sleep problems, and cognitive impairments and can increase the risk for suicide, posttraumatic stress disorder, depression, and anxiety. In some people, repetitive mTBI can lead to chronic traumatic encephalopathy (CTE), a neurodegenerative disorder. Evidence-based treatments are needed for both mTBI and CTE. Currently, symptom management and education are the best strategies to help those who have received multiple concussions. Prevention education about concussions and the use of return-to-play guidelines are especially important for young athletes. From the Veterans Affairs (VA) Northwest Network Mental Illness Research, Education, and Clinical Center (MIRECC); Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine; and the University of Washington Alzheimer's Disease Research Center, Seattle (Dr Peskind); Department of Neurology, Washington University School of Medicine, St. Louis, Missouri (Dr Brody); Military and Veterans†Clinical Rehabilitation Research, Faculty of Rehabilitation Medicine, University of Alberta, Edmonton, Alberta, Canada (Dr Cernak); Departments of Neurology and Pathology, VA Boston, and the Center for the Study of Traumatic Encephalopathy and the Alzheimer's Disease Center, Boston University, Boston, Massachusetts (Dr McKee); and Neurology Service, Cleveland VA Medical Center, Cleveland, Ohio (Dr Ruff).†‹
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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 | 0.000 | 0.002 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".