Temporal Changes in Quality Indicators in a Regional System of Care After Surgical and Transcatheter Aortic Valve Replacement
Bibliographic record
Abstract
Background: Historically, quality-of-care monitoring was performed separately for transcatheter and surgical aortic valve replacement (TAVR, SAVR). Using consensus indicators, we provide a global report on the quality of care for treatment of aortic stenosis across the highest-volume treatments: transfemoral (TF) TAVR, isolated SAVR, and SAVR combined with coronary artery bypass graft. Methods: Retrospective observational cohort study of consecutive patients in a regional system of care. Primary endpoint was 30-day and 1-year mortality (2015-2019). Secondary endpoints included rate of new pacemaker, rate of readmission, and length of stay (2012-2019). Following multivariable logistic regressions, we developed mortality case-mix adjustment models to report risk estimates. Results: = 0.09), respectively; surgical mortality rates for isolated SAVR and SAVR combined with coronary artery bypass graft were low and did not change significantly over time, ranging from 0.3% to 1.4% and from 0.9% to 3.4%, respectively at 30 days, and from 0.9% to 3.4% and from 4.7% to 6.7 at 1 year. In the TF TAVR cohort, the observed vs expected ratio for 30-day and 1-year mortality decreased significantly from 1.9 (95% confidence interval [CI] 0.9, 3.5) to 0.3 (95% CI 0.1, 0.8), and from 1.3 (95% CI 0.9, 1.7) to 0.7 (95% CI 0.5, 0.99), respectively; no change occurred in risk-adjusted surgical mortality. Conclusions: Consensus quality indicators provide unique insights on the quality of care for patients receiving treatment for aortic stenosis.
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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".