Association of single and multiple aneurysms with tobacco abuse: an @neurIST risk analysis
Bibliographic record
Abstract
OBJECTIVE: Although several studies have suggested that the incidence of intracranial aneurysms (IAs) is higher in smokers, the higher prevalence of subarachnoid hemorrhage (SAH) in smokers remains uncertain. It is unclear whether smoking additionally contributes to the formation of multiple aneurysms and the risk of rupture. The aim of this study was to determine whether smoking is associated with IA formation, multiplicity, or rupture. METHODS: Patients from the prospective multicenter @neurIST database (n = 1410; 985 females [69.9%]) were reviewed for the presence of SAH, multiple aneurysms, and smoking status. The prevalence of smokers in the population of patients diagnosed with at least one IA was compared with that of smokers in the general population. RESULTS: The proportion of smokers was higher in patients with IAs (56.2%) than in the reference population (51.4%; p < 0.001). A significant association of smoking with the presence of an IA was found throughout group comparisons (p = 0.01). The presence of multiple IAs was also significantly associated with smoking (p = 0.003). A trend was found between duration of smoking and the presence of multiple IAs (p = 0.057). However, the proportion of smokers among patients suffering SAH was similar to that of smokers among patients diagnosed with unruptured IAs (p = 0.48). CONCLUSIONS: Smoking is strongly associated with IA formation. Once an IA is present, however, smoking does not appear to increase the risk of rupture compared with IAs in the nonsmoking population. The trend toward an association between duration of smoking and the presence of multiple IAs stresses the need for counseling patients with IAs regarding lifestyle modification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".