Thrombus formation on bioprosthetic aortic valves
Bibliographic record
Abstract
Abstract Background 18F-GP1 is a novel radiotracer with a high affinity for the platelet glycoprotein IIb/IIIa receptor. Subclinical bioprosthetic valve thrombus has been postulated as a trigger for accelerated valve degeneration. Purpose To determine the feasibility of 18F-GP1 positron-emission tomography-computed tomography (PET-CT) for the detection of subclinical bioprosthetic aortic valve thrombus. Methods (i) Explanted degenerated aortic valve prostheses underwent histology and imaging. (ii) In a prospective observational study, patients with bioprosthetic aortic valve replacement (AVR) underwent echocardiography and 18F-GP1 PET-CT. Valves were assessed for hypoattenuating leaflet thickening (HALT), hypo-attenuation affection leaflet motion (HAM) and GP1 uptake. Results (i) GP1 correlated with thrombus on explanted valves (Figure). (ii) The first 6 patients (Table) were asymptomatic and had normally functioning surgical bioprostheses on echocardiography. At a median of 166 (range 122–189) days post-AVR, no patients had HALT or HAM on CT. There was avid focal GP1 uptake on the leaflets of all 6 patients which appeared most prominent along the leaflet edges (Figure). Only one patient had focal uptake in the valve frame, remote from the leaflets. In a separate cohort undergoing 18F-GP1 PET-CT for other conditions, there was no uptake on normal, native aortic valves (n=8). Conclusion For the first time, we demonstrate that 18F-GP1 PET-CT is a highly sensitive method of assessing platelet activation on bioprosthetic aortic valves. Despite the absence of CT evidence, early thrombus appeared to be a universal finding on recently implanted valve prostheses. The biological and clinical implications of subclinical bioprosthetic aortic valve thrombus have yet to be established. GP1 uptake in AVR Funding Acknowledgement Type of funding source: Foundation. Main funding source(s): British Heart Foundation
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; 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".