Performance of Computed Tomography Angiography (CTA) for the Diagnosis of Hypo-Attenuated Leaflet Thickening (HALT)
Bibliographic record
Abstract
(1) Background: Early hypo-attenuated leaflet thickening (HALT) is diagnosed by computed tomography angiography (CTA) in approximately 15% of patients undergoing transcatheter aortic valve replacement (TAVR). We sought to investigate the diagnostic performance of CTA for the diagnosis of HALT, focusing on timing data assessment within the cardiac cycle. (2) Methods: The study enrolled 50 patients with and 50 without HALT with available post-TAVR-CTA. The primary objective was to compare the diagnostic performance of CTA readings at specific intervals and time points during the cardiac cycle (entire systole, entire diastole, end-systole, and mid-diastole) versus gold standard (consensus reading by two observers based on multiphase full cardiac cycle data sets). (3) Results: 100 CTAs were independently analysed by two observers blinded to clinical characteristics of the study population and the results from the gold standard reading. Sensitivity and specificity for the diagnosis of HALT were 84%/94% in systole, 87%/92% in diastole, 78%/95% at end-systole, and 80%/94% at mid-diastole. End-systole had the highest positive predictive value (0.88) and positive likelihood ratio (36). Cohen's kappa for interobserver reliability was 0.715 in systole, 0.578 in diastole, 0.650 at end-systole, and 0.517 at mid-diastole. (4) Conclusion: Limiting CTA reading to distinct intervals or time points during the cardiac cycle has good specificity but lowers sensitivity. For a reliable diagnosis of HALT, data sets from a multiphase CTA covering the entire cardiac cycle should be analysed. A double reader approach would be desirable in further studies investigating HALT.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".