Teamwork makes dreamwork: patient-centred care includes rescue cardiac surgery during transcatheter aortic valve implantation
Bibliographic record
Abstract
Transcatheter aortic valve implantation (TAVI) has solidified its role in treating elderly patients with symptomatic, severe aortic stenosis who are considered at high risk or prohibitive risk and has rapidly expanded in many places to moderate- and low-risk patients in many places, including younger patient populations. With accrued experience, iterative TAVI device improvements, optimized use of supportive imaging, better patient selection and increasing care in specialized centres with well-functioning heart teams, TAVI procedures have become safer and patient outcomes continue to improve [1]. Nevertheless, TAVI procedures are not completely free of complications, which can occasionally be catastrophic and prompt emergency conversion to open-heart surgery. Marin-Cuartas et al. [2] from Leipzig Heart Centre have shared their vast experience of over 14 years treating 6903 patients with transfemoral TAVI and report the incidence and outcomes after emergency open-heart surgery during this period. They found that conversion to emergency surgery is infrequent (1.1%) and, as one would expect, its incidence has decreased over the years from a peak of 3.5% in the first time period to 0.4% in the most recent time period. As expected, the early and 1-year mortality and complication rates remain high in patients requiring emergency surgery; however, patients who were successfully rescued had a reasonable 87.5% survival at 1 year. Interestingly, when the authors stratified their analysis by risk profile, they reported an in-hospital mortality of 62.1% for high-risk patients and 12.5% for moderate/low-risk patients, highlighting that many of these patients can be successfully rescued from these catastrophic situations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.018 | 0.019 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".