Recent advances in nonoperating room anesthesia for cardiac procedures
Bibliographic record
Abstract
PURPOSE OF REVIEW: The number of complex procedures performed in the cardiac catheterization laboratory (CCL) is rapidly increasing. Because of their complexity, they frequently require the assistance of an anesthesiologist. The CCL is primarily designed to facilitate a percutaneous cardiac intervention; therefore, it might be a challenging workplace for an anesthesiologist. The aim of this review is to briefly present tasks and challenges of providing anesthesia in the CCL and to provide a concise description of common cardiac procedures performed there. RECENT FINDINGS: Recent literature indicates that many complicated cardiac procedures can be performed in CCL under monitored anesthesia care. At the same time several of them (e.g. transcatheter aortic valve replacement) are quickly becoming a viable alternative for surgical valve replacement. The most recent expansion of CCL procedures is related to rapidly growing population of grown-ups with congenital heart disease. All aforementioned developments present new challenges to an anesthesiologist. SUMMARY: New and fast development of percutaneous cardiac interventions has created a new working place for the anesthesiologist - the CCL. Our expertise in complex cardiac pathophysiology allows conduct of complicated procedures outside of the operating theater. For the same reasons, there is ongoing discussion whether anesthesia support in CCL should be provided by a general or cardiac anesthesiologist.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".