Angiographic and Clinical Outcomes in 200 Consecutive Patients with Cerebral Aneurysm Treated with Hydrogel-Coated Coils
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Denser coil packing in intracranial aneurysms is believed to result in lower recanalization rates. Hydrogel-coated expandable coils (HydroCoil) improve volumetric packing of aneurysms in animal models and clinical studies, but data from large clinical series are limited. The objective of this retrospective analysis was to analyze immediate and follow-up angiographic results as well as complications in a large consecutive series of patients treated with HydroCoils at a single institution. MATERIALS AND METHODS: Retrospective analysis was performed of periprocedural complications, immediate and follow-up angiograms, and retreatments of the first 200 consecutive intracranial aneurysms treated at Emory University Hospital. RESULTS: One hundred eighty-seven patients with 200 intracranial aneurysms were treated with HydroCoils during a 3-year period. Immediate angiograms showed complete aneurysmal obliteration in 58.4% of small aneurysms and 42.7% of large aneurysms. Periprocedural complications included early rebleeding and thromboembolic events resulting in permanent neurologic morbidity and mortality in 6% of cases. Follow-up angiography during an average of 16.3 months demonstrated recanalization in 17.7% of small aneurysms and 28.6% of large aneurysms, requiring retreatment in 6.3% and 19.0% of cases, respectively. During the same time period, there was delayed angiographic improvement in aneurysm obliteration in 26.6% of small aneurysms and 26.2% of large aneurysms. CONCLUSIONS: First-generation HydroCoil treatment of intracranial aneurysms has a favorable rate of recanalization compared with most large series of pure platinum coils with similar complication rates.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".