Patients With Small, Asymptomatic, Unruptured Intracranial Aneurysms and No History of Subarachnoid Hemorrhage Should Be Treated Conservatively
Bibliographic record
Abstract
t is well known that cerebral aneurysms are surprisingly prevalent in normal individuals and is estimated to be between 3.6% to 6.0% of the population. 1 The essential paradox appears to be the dichotomy between the epidemiological data and surgical experience.Wiebers' study 2 suggests that patients with unruptured cerebral aneurysms Ͻ7 mm in diameter have a benign natural history, but this contrasts with the experience of neurosurgeons, such as Weir, who are confronted by a substantial proportion of their patients with subarachnoid hemorrhage because of small ruptured aneurysms.3 How could this paradox be explained?It seems to us that we still have a very incomplete picture of the prevalence, size, distribution, and natural history of unruptured aneurysms over longer time epochs.Furthermore, it is quite possible that aneurysms may form quickly, as suggested by Weir, and rupture early during their expansion phase, although still quite small in diameter.The duration of this growth period to rupture is uncertain.There does seem to be reasonable evidence that aneurysms of Յ7 mm, once detected (at an uncertain time after their development) have a fairly low rate of rupture over a 5-year period. 2 Both clinicians and their patients, however, are concerned about lifetime risk, and long-term data are still lacking.In assessing the risks and benefits of intervention in a patient who is found to have an unruptured aneurysm, the treatment decision is also influenced by the current evolution in therapeutic strategies.In ruptured cerebral aneurysms, endovascular coiling was shown to be superior to surgery in a single randomized controlled trial with a relatively short follow-up of 12 months.4 It seems likely that these technologies will continue to improve, which will further alter the risk/benefit ratio of intervention in individual patients.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.015 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".