Revascularization for Complex Cerebral Aneurysms
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Complex cerebral aneurysms may require indirect treatment with revascularization. This manuscript describes various surgical revascularization techniques together with clinical outcomes. METHODS: Thirty-two consecutive patients with complex cerebral aneurysm were managed from November 2005 to October 2008. Techniques used for revascularization were high-flow bypass, low-flow bypass, branch artery reimplantion, and primary reanastomosis. Physiologic and anatomic monitoring technologies, including electroencephalography, somatosensory evoked potential monitoring, microvascular doppler ultrasonography, and/or indocyanine green videoangiography were used intraoperatively to assess both brain physiology and vascular anatomy. Patient outcome was determined using the Glasgow Outcome Scale at discharge and at a mean of 12 months post operation (range 6-25 months). RESULTS: Two cervical carotid aneurysms (6%) were resected followed by primary reanastomosis, 21 aneurysms (66%) were trapped following saphenous vein high-flow bypasses, five (16%) were clipped after superficial temporal or occipital artery low-flow bypasses, and four (12%) middle cerebral branch arteries were reimplanted. Of the 32 patients at discharge, 29 (91%) had a Glasgow Outcome Scale of four or five, two (6%) had severe disability, and one (3%) died. CONCLUSION: Cerebral revascularization remains an effective and reliable procedure for treatment of complex cerebral aneurysms. Low morbidity and mortality rates reflect the maturity of patient selection and surgical technique in the management of these lesions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".