European Society of Cardiology quality indicators for the care and outcomes of adults undergoing transcatheter aortic valve implantation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
AIMS: To develop a suite of quality indicators (QIs) for the evaluation of the care and outcomes for adults undergoing transcatheter aortic valve implantation (TAVI). METHODS AND RESULTS: We followed the European Society of Cardiology (ESC) methodology for the development of QIs. Key domains were identified by constructing a conceptual framework for the delivery of TAVI care. A list of candidate QIs was developed by conducting a systematic review of the literature. A modified Delphi method was then used to select the final set of QIs. Finally, we mapped the QIs to the EuroHeart (European Unified Registries on Heart Care Evaluation and Randomized Trials) data standards for TAVI to ascertain the extent to which the EuroHeart TAVI registry captures information to calculate the QIs. We formed an international group of experts in quality improvement and TAVI, including representatives from the European Association of Percutaneous Cardiovascular Interventions, the European Association of Cardiovascular Imaging, and the Association of Cardiovascular Nursing and Allied Professions. In total, 27 QIs were selected across 8 domains of TAVI care, comprising 22 main (81%) and 5 secondary (19%) QIs. Of these, 19/27 (70%) are now being utilized in the EuroHeart TAVI registry. CONCLUSION: We present the 2023 ESC QIs for TAVI, developed using a standard methodology and in collaboration with ESC Associations. The EuroHeart TAVI registry allows calculation of the majority of the QIs, which may be used for benchmarking care and quality improvement initiatives.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.017 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it