VARC endpoint definition compliance rates in contemporary transcatheter aortic valve implantation studies
Bibliographic record
Abstract
AIMS: The Valve Academic Research Consortium (VARC) endpoint definitions were established to standardise the reporting of clinical outcomes following transcatheter aortic valve implantation (TAVI). It remains unclear, however, to what extent and in which manner these definitions are applied. Therefore, we sought to investigate the utilisation and adherence to VARC guidelines since their introduction in 2011 across peerreviewed TAVI-related publications. METHODS AND RESULTS: We performed a systematic literature review to identify TAVI-related manuscripts published between February 2011 and February 2014. Manuscripts were categorised into three groups: a "compliant" group of manuscripts using only VARC-defined endpoints, a "non-compliant" group of manu scripts with only non-VARC-defined endpoints, and a "mixed compliant" group of manuscripts with both VARC- and non-VARC-defined endpoints. Multivariate analyses were performed to identify predictors of VARC use. Among 5,023 published manuscripts, 498 were included in the final analysis. At least one VARC definition was used in 275 (54%), while 223 (43%) did not use any VARC definitions. After publication of the first VARC manuscript (VARC-1, January 2011), VARC use increased from 31% (n=15) at six months to 69% (n=84) at 36 months. Following the publication of VARC-2 (October 2012), VARC-1 use declined (from 58% [n=47] to 36% [n=24]), while VARC-2 use increased from 4% (n=3) at six months to 35% (n=23) at 18 months. Of the manuscripts using VARC, 49 (10%) were classified as compliant and 226 (46%) as mixed compliant. The following endpoints were more often defined using VARC vs. non-VARC: myocardial infarction (64% vs. 36%); stroke (56% vs. 44%); bleeding (79% vs. 21%); vascular complications (70% vs. 30%); acute kidney injury (63% vs. 37%); reintervention (67% vs. 33%); and composite endpoints (52% vs. 48%). Mortality, valve dysfunction, TAVI-related complications, and quality of life were more often defined using non-VARC criteria. CONCLUSIONS: Implementation of VARC criteria in peer-reviewed manuscripts has increased over time. There remain, however, a considerable number (43%) of publications that do not report outcomes according to VARC. These data will inform the future development of VARC criteria.
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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 | 0.377 | 0.675 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.007 |
| Bibliometrics | 0.029 | 0.031 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".