Thrombogenicity assessment of surface-modified flow diverters: the impact of different surface modification strategies on thrombin generation in an acute <i>in vitro</i> test
Bibliographic record
Abstract
Background New generation flow-diverting stents have benefited from recent technological advances to reduce their thrombogenicity. This in vitro study is the first of its kind to compare multiple surface modified flow diverters with their bare metal counterparts. Methods A thrombin generation assay (TGA) was used to compare thrombin generation resulting from different stent types with glass beads (positive control) and plasma (negative control). Ten different stent types were studied, including a next-generation implant, Surpass Elite, with two different surface modifications. A thrombogram was generated from each of the 10 sample types, from which peak thrombin generation and time to peak (TTP) were obtained. Results Compared with the positive control and their bare metal counterparts, lower peak thrombin and longer TTP were obtained with most of the surface modified devices tested. Only the stent with an active heparin drug coating demonstrated lower peak thrombin and TTP than the negative control plasma. Conclusion Generally, surface modification resulted in lower thrombogenicity, as assessed by peak thrombin concentration and TTP, when compared with the unmodified version of the device. The device with an active heparin drug coating was significantly different from other surface modifications and plasma with respect to peak thrombin and TTP, though the implications of this should be investigated through future in vitro and in vivo studies.
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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 | 0.000 |
| 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".