Vascular Complications Among Patients Undergoing Trans-femoral Transcatheter Aortic Valve Implantation: Prostar <i>vs</i> ProGlide Parallel Technique
Bibliographic record
Abstract
Reliable femoral artery closure devices are essential for the success of trans-femoral Transcatheter Aortic Valve Implantation (TAVI) procedures. Accordingly, device choice might affect vascular complications and bleeding rates. This was a retrospective analysis, comparing vascular complication rates among patients who underwent trans-femoral TAVI with vascular access closure using either the ProGlide parallel suture or Prostar closure devices. We included 191 patients: 106 were treated with Prostar and 85 with ProGlide. The ProGlide group had higher rate of diabetes, chronic kidney disease, peripheral arterial disease, and significantly smaller femoral arteries that were treated via larger sheaths. Valve Academic Research Consortium (VARC)-2 major complications were similar between the groups. (4.7% for ProGlide vs 3.8% for Prostar, P=1), with similar incidence of closure device failure (2 vs 3, P=1). No differences were found after univariant analysis and propensity-score matching in the incidence of major and minor bleeding nor in the rate of in-hospital mortality between ProGlide and Prostar (4.7 vs 2.8%, P=.7, 1.2 vs 2.8%, P=.63, and 1.2 vs .0%, P=.45, respectively). Parallel suture technique using two ProGlide sutures showed comparable rates of vascular complications to the Prostar closure device in higher risk population of TAVI patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".