Effect of stent crimping on calcification of transcatheter aortic valves
Bibliographic record
Abstract
OBJECTIVES: Although many challenges related to the acute implantation of transcatheter aortic valves have been resolved, durability and early degeneration are currently the main concerns. Recent reports indicate the potential for early valve degeneration and calcification. However, only little is known about the underlying mechanisms behind the early degeneration of these valves. The goal of this study was to test whether stent crimping increases the risk for early calcification. METHODS: Stented valves that were crimped at 18-Fr and 14-Fr catheter and uncrimped controls were exposed to a standard calcifying solution for 50 million cycles in an accelerated wear test system. Subsequently, the leaflets of the valves were imaged by microcomputed tomography (micro-CT) followed by histochemical staining and microscopic analyses to quantify calcification and other changes in the leaflets' characteristics. RESULTS: Heavily calcified regions were found over the stent-crimped leaflets compared to uncrimped controls, particularly around the stent's struts. Micro-CT studies measured the total volume of calcification in the uncrimped valves as 77.31 ± 1.63 mm3 vs 95.32 ± 5.20 mm3 in 18-Fr and 110.01 ± 8.33 mm3 in 14-Fr stent-crimped valves, respectively. These results were congruent with the increase in leaflet thickness measured by CT scans (0.44 ± 0.07 mm in uncrimped valves vs 0.69 ± 0.15 mm and 0.75 ± 0.09 mm in 18-Fr and 14-Fr stent-crimped valves, respectively). Histological studies confirmed the micro-CT results, denoting that the percentage of calcification in uncrimped leaflets at the valve's posts was 5.34 ± 3.97 compared to 19.97 ± 6.18 and 27.64 ± 13.17 in the 18-Fr and 14-Fr stent-crimped leaflets, respectively. CONCLUSIONS: This study concludes that stent-crimping damage is associated with a higher level of passive leaflet calcification, which may contribute to early valve degeneration.
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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 | 0.000 | 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 | 0.000 |
| 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".