Motor Vehicle Accidents: the Most Common Cause of Traumatic Vertebrobasilar Ischemia
Bibliographic record
Abstract
BACKGROUND: Recent media exposure of strokes from chiropractic manipulation have focused attention on traumatic vertebrobasilar ischemia. However, chiropractic manipulation, while the easiest cause to recognize, is probably not the most common cause of this condition. METHODS: We reviewed all consecutive cases of traumatic vertebrobasilar ischemia referred to a single neurovascular practice over 20 years, from the office files and hospital records. RESULTS: There were 80 patients whose vertebrobasilar ischemia was attributed to neck trauma. Five were diagnosed as due to chiropractic manipulation, but the commonest attributed cause was motor vehicle accidents (MVAs), which accounted for 70 cases; one was a sports injury, and five were industrial accidents. In some cases neck pain from an MVA led to chiropractic manipulation, so the cause may have been compounded. In most vehicular cases the diagnosis had been missed, even denied, by the neurologists and neurosurgeons initially involved. The longest delay between the injury and the onset of delayed symptoms was five years. CONCLUSIONS: Traumatic vertebrobasilar ischemia is most often due to MVAs; the diagnosis is often missed, in part because of the delay between injury and onset of symptoms and, in part, we hypothesize, because of reluctance of doctors to be involved in medicolegal cases.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".