Immediate postcardiac arrest treatment: coronary catherization or not?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Early coronary angiogram (CAG) remains a cornerstone in postcardiac arrest management as coronary disease (CAD)-related cardiac arrest is the leading cause of sudden death in adults. The opportunity to treat the cause early on with immediate CAG may improve outcome in cardiac arrest patients with AMI. Identifying the patients who will benefit from such an early invasive strategy is an unanswered question. Recent and ongoing trials may improve the level of evidence on this problematic, especially for some subgroup; however, current guidelines remain founded upon a very heterogeneous level of evidence. RECENT FINDINGS: The key variable to argue for immediate CAD remains the pattern of the ECG monitored after return of spontaneous of circulation (ROSC). ST-segment elevation (STE) on postresuscitation ECG is the strongest argument to rule for an early CAG strategy. In other situations, identifying the best candidates for early CAG is very challenging. Different approaches including elements, such as circumstances of cardiac arrest and expected outcomes. may also drive the strategy. SUMMARY: This review aims to provide an overview of these different discussion points. The indication for early CAG should rely on multiple factors and an individual approach.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".