Management nicht rupturierter zerebraler Aneurysmen
Bibliographic record
Abstract
Unruptured intracranial aneurysms (UIAs) are a common coincidental finding in cranial imaging of patients with non-correlated symptoms such as headache or dizziness. With an estimated prevalence of around 1 - 2 % in the general population, these UIAs often present clinicians with difficult decisions. This is particularly the case since, despite extensive research in this area, the natural course of UIAs is still poorly understood and the risk of rupture cannot be specified. Due to often catastrophically clinical outcomes as a result of an aneurysmal subarachnoid haemorrhage (mortality-rates of up to 51 %), the desire for intervention and the emotional burden on the patient in the case of diagnosis of an UIA is often very high. For this reason, the knowledge of average rupture rates, factors that influence them, but also knowledge of the complication rates and the result of interventions is essential for the clinician in order to arrive together with the patient at a responsible and reasonable decision regarding the treatment of an UIA. In this review, we present the current state of science regarding the natural course of UIAs, the possibilities of intervention and strategies in patient management based on current guidelines.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.002 | 0.001 |
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".