Bibliographic record
Abstract
PURPOSE OF REVIEW: The role of anesthesia outside the operating room is rapidly expanding and evolving alongside with the advances in interventional neuroradiology. Increasingly complex diagnostic and therapeutic neuroradiological procedures are being performed on sicker patients. This review provides an overview of the principles of anesthetic management and summarizes recent advances in interventional neuroradiology. RECENT FINDINGS: There are many new areas of development in interventional neuroradiology, but each also brings with it controversy. Use of newer agents for anesthesia and for anticoagulation may change the intraoperative management of patients. The role of neurophysiological monitoring during endovascular procedures is still to be validated. The optimal mode of treating cerebral aneurysms is still being debated. There has been increasing interest in and evidence of the efficacy of carotid artery stenting in the treatment of carotid artery disease. The utility of intraoperative magnetic resonance imaging in neurosurgery is expanding rapidly. SUMMARY: Providing anesthesia in the interventional neuroradiology suite continues to be a challenge to the anesthesiologist. Understanding the anesthetic constraints and complexities and keeping abreast of the current developments in neuroradiology are crucial in ensuring the maximal benefits to and safety of 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.041 | 0.033 |
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".