Setting a benchmark for resource utilization and quality of care in patients undergoing transcatheter aortic valve implantation in Europe—Rationale and design of the international <scp>BENCHMARK</scp> registry
Bibliographic record
Abstract
BACKGROUND: The use of transcatheter aortic valve implantation (TAVI) for treating aortic stenosis (AS) has increased exponentially in recent years. Despite the availability of clinical practice guidelines for the management of valvular heart disease, disparities in quality of care (QoC) for TAVI patients remain widespread across Europe. Tailored QoC measures will help to reduce resource utilization and improve patient outcomes without compromising patient safety. Using a clear set of QoC measures, the BENCHMARK registry aims to document the progress that can be achieved if such tailored QoC measures are implemented. METHODS: The BENCHMARK registry (BENCHMARK) is a non-interventional, multicenter registry in patients with severe symptomatic AS undergoing TAVI with a 1- and 12-months follow-up. BENCHMARK will be conducted at 30 centers across Europe and will enroll a total of 2400 consecutive TAVI patients. Patients suffering from severe symptomatic AS who undergo TAVI with a balloon-expandable transcatheter aortic valve will be included. The registry will comprise four phases: (1) a retrospective baseline evaluation phase; (2) an education phase; (3) an implementation phase; and (4) a prospective effect documentation phase (prospective phase). The registry's primary objectives are to reduce the length of hospital stay and accelerate the post-procedural patient recovery pathway, but without compromising safety. The study started in April 2021 and has an estimated completion date of May 2023. DISCUSSION: BENCHMARK will establish QoC measures to reduce resource utilization, intensive care unit bed occupancy, and overall length of hospitalization with uncompromised patient safety post-TAVI (ClinicalTrials.gov Identifier: NCT04579445).
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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 | 0.088 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".