Microsurgical Vascular Manipulation in Aneurysm Surgery and Delayed Ischemic Injury
Bibliographic record
Abstract
BACKGROUND: The role of aggressive surgical manipulation with clot evacuation, arachnoid dissection, and papaverine-guided adventitial dissection of large vessels during ruptured aneurysm surgery in reducing vasospasm is controversial. Here we describe a single-institution experience in aneurysm surgery outcomes with and without aggressive surgery. METHODS: We performed retrospective analysis of all patients >18 years of age with subarachnoid hemorrhage (SAH) from anterior circulation aneurysms between 2008 and 2013 at the University of New Mexico Hospital. Vasospasm was characterized on days 3 through 14 after SAH based on: (1) angiography, (2) vasospasm requiring angiographic intervention, (3) development of delayed ischemic neurologic deficit (DIND), and (4) radiological appearance of new strokes. RESULTS: Of 159 patients, 114 (71.6%) had "aggressive" and 45 (28.3%) had standard microsurgery. More than 60% of patients presented with a Hunt and Hess score of ≥3 and a Fisher grade (FG) of 4. Compared with standard surgery, there was a statistically significant decrease in the incidence of DIND in patients undergoing aggressive surgery (18.4% vs 37.8%, p=0.01). Moreover, there was a reduction in the number of new strokes by 30% in the aggressive surgery group with moderate or higher degrees of vasospasm (46.0% vs 76.5%, p=0.06). In the same group with FG 4 SAH, however, this difference was more than 50% (30% vs 64.7%, p=0.02). CONCLUSIONS: We conclude that aggressive surgical manipulation during aneurysm surgery results in lower incidence of DIND and new strokes. This effect is most pronounced in patients with FG 4 SAH.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".