Transcatheter aortic valve resection: new mechanical devices
Bibliographic record
Abstract
Background: To improve periprocedural outcomes of transcatheter aortic valve implantation (TAVI), transcatheter mechanical resection devices were tested for prior ablation of the aortic cusps. Methods: Three mechanical transcatheter resection devices were tested in a series of native porcine (n=30) and reassembled calcified human valves (n=54). The resection time, the resected valve area, the number of released cusps, and the degree of surrounding tissue damage were measured. Afterwards, postmortem transapical-transcatheter-resections of the aortic valve in two humans were performed. Results: In the native porcine hearts, the Aesculap II device demonstrated significantly shorter resection time compared to the R&R II and the Randstad devices (6.5±2.0 vs. 28.6±24.1 vs. 23.3±14.4 sec; P=0.001). However, it created more lesions in the surrounding tissue (P=0.002). The R&R II achieved a smaller number of resected cusps than the other two devices (2.7±0.7 vs. 1.1±0.7 vs. 2.4±0.5; P<0.001, respectively). It also resected a smaller area of the aortic valve (306.5±149.2 vs. 106.7±29.6 vs. 256.8±81.3 mm2; P=0.09) but a larger mean area of the resected fragments (110.3±41.5 vs. 160.7±29.6 vs. 111.5±43.9 mm2; P=0.01). The resection of the reassembled human valves demonstrated the same results between the devices regarding resection time (P=0.001) and resected area (P=0.016), but not fragment sizes (P=0.610). Finally, transapical-transcatheter-resection of aortic valve was performed in two cadavers. Conclusions: Transcatheter aortic valve resection is feasible with variable aortic leaflet resection times and mild risk of lesions of the surrounding tissue.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".