Unruptured intracranial aneurysms: natural history and management decisions
Bibliographic record
Abstract
Aneurysmal subarachnoid hemorrhage (SAH) carries a grim prognosis, with high mortality and morbidity rates. The mortality rate in the first 30 days postrupture is estimated to be in the range of 40 to 50%, and almost half of the survivors will be left with a neurological deficit. Unlike patients with aneurysmal SAH, those with unruptured intracranial aneurysms usually experience no neurological deficit, and their treatment is prophylactic, aiming to reduce the risk of future bleeding and its consequences. The risk of rupture therefore assumes special importance when making decisions regarding which patient or aneurysm to treat. In previous reports the risk of bleeding for unruptured aneurysms has been stated as approximately 2% per year. The retrospective part of the International Study of Unruptured Intracranial Aneurysms (ISUIA) reported very low annual bleeding rates (0.05-1%) and high surgical morbidity and mortality rates (8-18%), prompting discussion in which the benefits of prophylactic treatment in the majority of these lesions were questioned. Prospective data from the second part of the ISUIA recently included rupture rates ranging from 0 to 10% per year. The aim of this paper was to review the evidence that is currently available for neurosurgeons to use when making decisions regarding patients who would benefit from treatment of an unruptured intracranial aneurysm.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".