Final Results of the Prospective Multicenter Excimer Laser-Assisted High-Flow Bypass Study on the Treatment of Giant Anterior Circulation Aneurysms
Bibliographic record
Abstract
BACKGROUND: Both conventional bypass utilizing temporary recipient vessel occlusion and the excimer laser-assisted nonocclusive anastomosis (ELANA) bypass technique are possible strategies in the treatment of giant aneurysms. These treatments have only been studied in single institutional retrospective studies. The potential advantage of the ELANA technique is the absence of temporary occlusion of major arteries, decreasing the risk of intraoperative ischemia. OBJECTIVE: To investigate the risks and potential benefits of high-flow bypass surgery for giant and complex aneurysms of the anterior cerebral circulation. In addition, the effectiveness of the ELANA bypass procedure in the treatment of these aneurysms is determined. METHODS: A total of 37 patients were included in 8 vascular neurosurgical centers in the United States, Canada, and Europe. A 30-d postoperative bypass follow-up was studied by using digital subtraction angiography and/or magnetic resonance angiography and computed tomography angiography to assess patency as well as by clinical monitoring in all patients. RESULTS: In 35 patients, an ELANA high-flow bypass was performed and the aneurysm treated. Four patients had remaining neurological deficits after 30 d caused by stroke (11.4%). These strokes were not related to the ELANA anastomosis device. CONCLUSION: This study does not prove that the ELANA technique has an advantage over conventional bypass techniques, but it appears to be an acceptable alternative to conventional transplanted high-flow bypass in this very-difficult-to-treat patient group, especially in select patients whom cannot be bypassed using conventional means in which temporary occlusion is considered to be not recommended.
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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 | 0.001 | 0.002 |
| 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".