Safety and efficacy of treatment of very small intracranial aneurysms
Bibliographic record
Abstract
PURPOSE: Very small intracranial aneurysms (VSIAs) may cause many neurological complications and even death. Thanks to technological progress and higher quality of non-invasive neuroimaging methods, these pathologies can be investigated sooner and treated earlier. Due to the controversy surrounding invasive treatment of these pathologies, the aim of the study was to analyse methods of treatment, their outcome, and complications in a group of patients with VSIAs. MATERIAL AND METHODS: Out of 444 cases of intracranial aneurysms treated in our centre, 65 aneurysms met the radiological criteria of VSIAs. The parameters - width and length of the aneurysm's neck and width, length, and height of the aneurysm's dome - were measured. The analysed parameters were as follows: symptoms upon admission and after treatment, days in hospital, and intraoperative complications. Clinical and radiological intensity of subarachnoid haemorrhage (SAH) was evaluated by using the Hunt-Hess and Fisher scales. The degree of embolisation of the aneurysm after the procedure was assessed using the Montreal Scale. Clinical outcome was assessed by Glasgow Outcome Scale. RESULTS: 50.77% of VSIAs were treated with endovascular procedures and 49.23% with neurosurgical clipping. SAH was presented in 38.46% of patients with VSIAs. Intraoperative complications were presented in 16.92% of patients with VSIAs, and the most common complication was ischaemic stroke. Stents were used in 51.52% of VSIAs. In 69.70% of embolisation procedures at VSIAs complete obliteration was achieved. The average result in the Montreal Scale was 1.31 (SD = 0.66). CONCLUSION: VSIAs can be treated as effectively and safely as larger aneurysms, by both endovascular and surgical methods.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".