Oops, we did it again and all together!
Bibliographic record
Abstract
As the cold December days set in and the Holiday season begins, what better time to settle in with the latest issue of the European Heart Journal—Acute Cardiovascular Care? This December edition brings together an exceptional array of research spanning the full spectrum of acute cardiology and intensive care. With a special focus on thromboembolic disease, it builds on knowledge showcased in previous publications over the last two years,1–8 that have explored right ventricular adaptation, advanced imaging, risk stratification, and mechanical circulatory support in pulmonary embolism. Together, these manuscripts contribute to an evolving picture of how we can continue to refine diagnosis, intervention, and post-acute management to improve outcomes for our most vulnerable patients. Our December Editor’s Choice comes from Monil Majmundar, MD, and colleagues,9 who analysed 14 731 propensity-matched patients from the 2021 National Readmission Database to compare endovascular mechanical thrombectomy (MT) and catheter-directed thrombolysis (CDT) in pulmonary embolism. MT was associated with higher in-hospital mortality (4.4% vs. 3.4%; OR 1.31; 95% CI 1.01–1.68) and major bleeding (6.3% vs. 3.6%; OR 1.79; 95% CI 1.39–2.32), though post-discharge mortality was similar. Notably, high-volume centres demonstrated lower mortality and bleeding rates, narrowing the gap between MT and CDT—underscoring both the crucial role of procedural expertise and institutional experience in interventional PE therapy and the needs for high-quality data from ongoing and future large randomized controlled trials. These real-world insights, accompanied by a probing editorial from Marco Roffi, provide critical context for clinicians navigating this rapidly evolving field.10
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 | 0.004 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.089 | 0.063 |
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".