Growth and regression of an intracranial vertebral artery dissecting aneurysm
Bibliographic record
Abstract
Intracranial vertebral artery dissecting aneurysms (VADAs) are rare vascular abnormalities with diverse presentations and unpredictable natural histories. Traditionally considered aggressive lesions with high mortality, emerging evidence has suggested some unruptured cases may undergo stabilization or even regression. This report details a 47-year-old patient presenting with ataxia and neck pain following a presumed traumatic dissection, leading to a diagnosis of a right vertebral artery dissection with mural hematoma formation. Serial imaging over two-years demonstrated progressive aneurysmal growth with mass effect at 6 weeks and 9 weeks, followed by stabilization at 12 months and subsequent complete regression of the aneurysm by 24 months. Conservative management was pursued due to patient preference, highlighting the importance of patient selection in decision making for VADAs. The observed spontaneous regression likely reflects a combination of mural hematoma reabsorption, closure of the dissecting flap, and robust collateral circulation. This case contributes to the evolving understanding of intracranial dissection aneurysms, emphasizing the potential for self-healing in select cases while reinforcing the need for individualized treatment strategies.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".