A Review of Recent Advances in Endovascular Therapy for Intracranial Aneurysms
Bibliographic record
Abstract
Despite the advances in neuro-interventional techniques and expertise to treat intracranial aneurysms (IAs), there remains a subset of IAs that are considered to be a significant treatment challenge. Working closely with the neuro-interventional community, bioengineers have harnessed their knowledge of anatomy, physiology, biophysics, and new materials to develop novel therapeutic adjuncts for the successful endovascular treatment of simple and complex IAs. This review describes the biological challenges, the landscape of neuro-interventional management of IAs, and the factors pertinent to which therapeutic modality is recommended. Finally, recent technological advances that have emerged over the last decade are discussed, taking the reader through the devices' objectives, utility, and safety profiles. The goal of this review is to (i) provide physicians treating IAs with the pertinent information to facilitate evidence-based clinical decision thereby minimizing morbidity and mortality and (ii) facilitate professionals in the biomedical engineering field with the clinical background and summarize current endovascular IA treatment options available, with the intent to inspire future IA device development and innovation.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".