Subclinical valve leaflet thrombosis following bioprosthetic aortic valve replacement
Bibliographic record
Abstract
PURPOSE OF REVIEW: Subclinical leaflet thrombosis (SLT) is often an incidental finding characterized by a thin layer of thrombus involving one, two or three leaflets, with typical appearance on multi-detector computed tomography (MDCT) of hypo-attenuating defect at the aortic side of the leaflet, also called hypo-attenuating leaflet thickening (HALT). SLT may occur following both transcatheter aortic replacement (TAVR) or biological surgical aortic valve replacement (SAVR). The aim of this review is to present an overview of the current state of knowledge on the incidence, diagnosis, clinical impact, and management of SLT following TAVR or SAVR. RECENT FINDINGS: SLT occurs in 10-20% of patients following TAVR and is somewhat more frequent than following SAVR (5-15%). SLT may regress spontaneously without treatment in about 50% of the cases but may also progress to clinically significant valve thrombosis in some cases. Oral anticoagulation with vitamin K antagonist is reasonable if SLT is detected by echocardiography and/or MDCT during follow-up and is generally efficient to reverse SLT. SLT is associated with mild increase in the risk of stroke but has no impact on survival. SLT has been linked with accelerated structural valve deterioration and may thus impact valve durability and long-term outcomes. SUMMARY: SLT is often an incidental finding on echocardiography or MDCT that occurs in 10-20% of patients following TAVR or 5-15% following biological SAVR and is associated with a mild increase in the risk of thrombo-embolic event with no significant impact on mortality but may be associated with reduced valve durability.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".