Safety and Effectiveness of Radioactive Coil Embolization of Aneurysms
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Recanalization after coil embolization can be prevented by radiation emitted from 32P coils. We wanted to determine the upper limits of 32P activities that could be implanted onto coils with respect to the potential injury to nearby nerves, delay in organization of the clot, and effects on neointima formation and recanalization. METHODS: We studied the effects of various 32P activities on recanalization and organization of thrombus after coil occlusion of canine arteries and on neointima formation at the neck of canine carotid bifurcation aneurysms. We also tested potential injury to nerves in the vicinity of radioactive or nonradioactive coils in 3 models: the brachial plexus (near proximal vertebral arteries) and the lingual nerve in a lingual artery bifurcation aneurysm model, both models being treated by radioactive or standard coil occlusion. Finally, we wrapped lingual nerves with nonradioactive or high-activity coils and studied their effects on lingual nerves and tongues. Results were assessed with a pathological scoring system and compared with Mann-Whitney and Kruskal-Wallis tests. RESULTS: No deleterious effect of radiation on nerves could be detected. Neointima formation was not hampered, scores of aneurysms treated with 32P-coils being significantly better when compared with treatments with standard coils (P=0.002). Arteries treated with high-activity coils (>3.39 microCi) showed absent recanalization but delayed organization of the clot at 3 months compared with low-activity or nonradioactive coils (P<0.05). CONCLUSIONS: beta-Radiation can prevent recanalization after coil occlusion. We could not demonstrate any deleterious effects of radioactivity on nervous structure or on neointima formation. Delayed organization of thrombus provides a rational basis to establish an upper limit for 32P activities to be implanted onto 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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".