The problem of axonal injury in the brains of veterans with histories of blast exposure
Bibliographic record
Abstract
INTRODUCTION: Blast injury to brain, a hundred-year old problem with poorly characterized neuropathology, has resurfaced as health concern in recent deployments in Iraq and Afghanistan. To characterize the neuropathology of blast injury, we examined the brains of veterans for the presence of amyloid precursor protein (APP)-positive axonal swellings typical of diffuse axonal injury (DAI) and compared them to healthy controls as well as controls with opiate overdose, anoxic-ischemic encephalopathy, and non-blast TBI (falls and motor vehicle crashes). RESULTS: In cases with blast history, we found APP (+) axonal abnormalities in several brain sites, especially the medial dorsal frontal white matter. In white matter, these abnormalities were featured primarily by clusters of axonal spheroids or varicosities in a honeycomb pattern with perivascular distribution. Axonal abnormalities colocalized with IBA1 (+) reactive microglia and had an appearance that was distinct from classical DAI encountered in TBI due to motor vehicle crashes. Opiate overdose cases also showed APP (+) axonal abnormalities, but the intensity of these lesions was lower compared to cases with blast histories and there was no clear association of such lesions with microglial activation. CONCLUSIONS: Our findings demonstrate that many cases with history of blast exposure are featured by APP (+) axonopathy that may be related to blast exposure, but an important role for opiate overdose, antemortem anoxia, and concurrent blunt TBI events in war theater or elsewhere cannot be discounted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".