Prevalence, Assessment, and Treatment of Mild Traumatic Brain Injury and Posttraumatic Stress Disorder
Bibliographic record
Abstract
BACKGROUND: Iraq and Afghanistan war veterans are returning from combat having sustained traumatic brain injury, mostcommonly mild traumatic brain injury (mTBI), and experiencing posttraumatic stress disorder (PTSD). Clinical guidelines for mTBI and PTSDdo not focus on the co-occurrence of these conditions (mTBI/PTSD). A synthesis of the evidence on prevalence, diagnostic accuracy, andtreatment effectiveness for mTBI/PTSD would be of use to clinicians, researchers, and policymakers. METHODS: We conducteda systematic review of studies identified through PubMed, PsycINFO, REHABDATA, Cochrane Library, pearling, and expert recommendations. Peer-reviewed English language studies published between 1980 and June, 2009 were included if they reported frequencies of traumatic braininjury and PTSD, or diagnostic accuracy or treatment effectiveness specific to mTBI/PTSD. RESULTS: Thirty-four studies metinclusion criteria. None evaluated diagnostic accuracy or treatment effectiveness. Studies varied considerably in design. Frequency ofmTBI/PTSD ranged from 0% to 89%. However, in 3 large studies evaluating Iraq and Afghanistan war veterans, frequencies ofprobable mTBI/PTSD were from 5% to 7%; among those with probable mTBI, frequencies of probable PTSD were from 33% to 39%. DISCUSSION: The wide range of mTBI/PTSD frequency levels was likely due to variation across studyparameters, including aims and assessment methods. Studies using consistent, validated methods to define and measure mTBI history andPTSD 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 | 0.022 | 0.111 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| 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) | 0.002 | 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".