Surgical clipping or endovascular coiling for unruptured intracranial aneurysms: a pragmatic randomised trial
Bibliographic record
Abstract
BACKGROUND: Unruptured intracranial aneurysms (UIAs) are increasingly diagnosed and are commonly treated using endovascular treatment or microsurgical clipping. The safety and efficacy of treatments have not been compared in a randomised trial. How to treat patients with UIAs suitable for both options remains unknown. METHODS: We randomly allocated clipping or coiling to patients with one or more 3-25 mm UIAs judged treatable both ways. The primary outcome was treatment failure, defined as: initial failure of aneurysm treatment, intracranial haemorrhage or residual aneurysm on 1-year imaging. Secondary outcomes included neurological deficits following treatment, hospitalisation >5 days, overall morbidity and mortality and angiographic results at 1 year. RESULTS: The trial was designed to include 260 patients. An analysis was performed for slow accrual: 136 patients were enrolled from 2010 through 2016 and 134 patients were treated. The 1-year primary outcome, available for 104 patients, was reached in 5/48 (10.4% (4.5%-22.2%)) patients allocated surgical clipping, and 10/56 (17.9% (10.0%-29.8%)) patients allocated endovascular coiling (OR: 0.54 (0.13-1.90), p=0.40). Morbidity and mortality (modified Rankin Scale>2) at 1 year occurred in 2/48 (4.2% (1.2%-14.0%)) and 2/56 (3.6% (1.0%-12.1%)) patients allocated clipping and coiling, respectively. New neurological deficits (15/65 vs 6/69; OR: 3.12 (1.05-10.57), p=0.031), and hospitalisations beyond 5 days (30/65 vs 6/69; OR: 8.85 (3.22-28.59), p=0.0001) were more frequent after clipping. CONCLUSION: Surgical clipping or endovascular coiling of UIAs did not show differences in morbidity at 1 year. Trial continuation and additional randomised evidence will be necessary to establish the supposed superior efficacy of clipping.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".