Intracranial saccular aneurysm enlargement determined using serial magnetic resonance angiography
Bibliographic record
Abstract
OBJECT: The goal of this study was to determine the frequency of enlargement of unruptured intracranial aneurysms by using serial magnetic resonance (MR) angiography and to investigate whether aneurysm characteristics and demographic factors predict changes in aneurysm size. METHODS: A retrospective review of MR angiograms obtained in 57 patients with 62 unruptured, untreated saccular aneurysms was performed. Fifty-five of the 57 patients had no history of subarachnoid hemorrhage. The means of three measurements of the maximum diameters of these lesions on MR source images defined the aneurysm size. The median follow-up period was 47 months (mean 50 months, range 17-90 months). No aneurysm ruptured during the follow-up period. Four patients (7%) harbored aneurysms that had increased in size. No aneurysms smaller than 9 mm in diameter grew larger, whereas four (44%) of the nine aneurysms with initial diameters of 9 mm or larger increased in size. Factors that predicted aneurysm growth included the size of the lesion (p < 0.001) and the presence of multiple lobes (p = 0.021). The location of the aneurysm did not predict an increased risk of enlargement. CONCLUSIONS: Patients with medium-sized or large aneurysms and patients harboring aneurysms with multiple lobes may be at increased risk for aneurysm growth and should be followed up with MR imaging if the aneurysm is left untreated.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".