Treatment of Intracranial Aneurysms with Hydrogel Coated Expandable Coils
Bibliographic record
Abstract
BACKGROUND: Coiling of intracranial aneurysms with platinum coils sometimes results in relatively poor angiographic results which may be is related to low packing volumes achieved. Hydrogel coated expandable coils (HydroCoil) have been shown to achieve better aneurysm volume filling which may potentially result in lower recanalization rates. Currently there is limited clinical data on their safety and efficacy in aneurysm treatment. METHODS: We analyzed data from a prospectively collected database on patients treated at the Toronto Western Hospital. The analysis included the patients' characteristics, aneurysm size, packing, procedure related complications, recanalization and clinical outcome. RESULTS: Twenty-nine aneurysms were treated with HydroCoils only or in combination with other coils. The average calculated filling of the aneurysm volume was 74-76%. On the immediate post treatment angiograms, 44% of the berry type aneurysms were completely obliterated, 33% had a residual neck and, in 20%, a residual aneurysm was seen. Follow-up imaging was available in 23 cases. On imaging follow-up (from 2 days to 11 months) one dissecting aneurysm had recanalized. There were six technical/medical complications with no clinical consequences. Two clinically significant procedural related complications occurred. CONCLUSIONS: HydroCoils can be used effectively to treat intracranial aneurysms. The volume expansion allows for much greater packing than described for bare platinum coils, which may result in better long-term results. The recanalization rate is low but the limited follow-up does not allow for any conclusion regarding the long-term outcome. The complication rate is similar to larger current series using bare platinum coils.
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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 | 0.001 | 0.001 |
| 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".