PHASES Score for Prediction of Intracranial Aneurysm Growth
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Growth of an intracranial aneurysm occurs in around 10% of patients at 2-year follow-up imaging and may be associated with aneurysm rupture. We investigated whether PHASES, a score providing absolute risks of aneurysm rupture based on 6 easily retrievable risk factors, also predicts aneurysm growth. METHODS: In a multicenter cohort of patients with unruptured intracranial aneurysms and follow-up imaging with computed tomography angiography or magnetic resonance angiography, we performed univariable and multivariable Cox regression analyses for the predictors of the PHASES score at baseline, with aneurysm growth as outcome. We calculated hazard ratios and corresponding 95% confidence intervals (CI), with the PHASES score as continuous variable and after division into quartiles. RESULTS: We included 557 patients with 734 unruptured aneurysms. Eighty-nine (12%) aneurysms in 87 patients showed growth during a median follow-up of 2.7 patient-years (range 0.5-10.8). Per point increase in PHASES score, hazard ratio for aneurysm growth was 1.32 (95% CI, 1.22-1.43). With the lowest quartile of the PHASES score (0-1) as reference, hazard ratios were for the second (PHASES 2-3) 1.07 (95% CI, 0.49-2.32), the third (PHASES 4) 2.29 (95% CI, 1.05-4.95), and the fourth quartile (PHASES 5-14) 2.85 (95% CI, 1.43-5.67). CONCLUSIONS: Higher PHASES scores were associated with an increased risk of aneurysm growth. Because higher PHASES scores also predict aneurysm rupture, our findings suggest that aneurysm growth can be used as surrogate outcome measure of aneurysm rupture in follow-up studies on risk prediction or interventions aimed to reduce the risk of rupture.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".