Survival Analysis of Risk Factors for Major Recurrence of Intracranial Aneurysms after Coiling
Bibliographic record
Abstract
BACKGROUND: Recurrence after intracranial aneurysm coiling is a highly prevalent outcome, yet to be understood. We investigated clinical, radiological and procedural factors associated with major recurrence of coiled intracranial aneurysms. METHODS: We retrospectively analyzed prospectively collected coiling data (2003-12). We recorded characteristics of aneurysms, patients and interventional techniques, pre-discharge and angiographic follow-up occlusion. The Raymond-Roy classification was used; major recurrence was a change from class I or II to class III, increase in class III remnant, and any recurrence requiring any type of retreatment. Identification of risk factors associated with major recurrence used univariate Cox Proportional Hazards Model followed by multivariate regression analysis of covariates with P<0.1. RESULTS: A total of 467 aneurysms were treated in 435 patients: 283(65%) harboring acutely ruptured aneurysms, 44(10.1%) patients died before discharge and 33(7.6%) were lost to follow-up. A total of 1367 angiographic follow-up studies (range: 1-108 months, Median [interquartile ranges (IQR)]: 37[14-62]) was performed in 384(82.2%) aneurysms. The major recurrence rate was 98(21%) after 6(3.5-22.5) months. Multivariate analysis (358 patients with 384 aneurysms) revealed the risk factors for major recurrence: age>65 y (hazard ratio (HR): 1.61; P=0.04), male sex (HR: 2.13; P<0.01), hypercholesterolemia (HR: 1.65; P=0.03), neck size ≥4 mm (HR: 1.79; P=0.01), dome size ≥7 mm (HR: 2.44; P<0.01), non-stent-assisted coiling (HR: 2.87; P=0.01), and baseline class III (HR: 2.18; P<0.01). CONCLUSION: Approximately one fifth of the intracranial aneurysms resulted in major recurrence. Modifiable factors for major recurrence were choice of stent-assisted technique and confirmation of adequate baseline occlusion (Class I/II) in the first coiling procedure.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".