Effects of adaptive or fixed thresholds and different platforms on the assessment of plaque characteristics using coronary computed tomography angiography
Bibliographic record
Abstract
Background Coronary computed tomography angiography (CCTA) is used to evaluate components of atherosclerosis. Either adaptive or diverse, fixed Hounsfield Units (HU) are used to define components such as low attenuation (LAP), mixed (MP) and calcified plaque (CP). Comparisons of different platforms and different thresholding approaches have not been extensively evaluated. We compare two fixed threshold options to an adaptive threshold option within a specific platform and to fixed threshold options measured with another platform. Methods Coronary segments (n = 24) of good image quality, with well-defined boundaries and representing a broad range of atheroma were analyzed for LAP, MP and CP. Thresholds for LAP vs MP and MP vs CP were either Fixed 30/350 , Fixed 75/350 or based on an automatically determined Adaptive option. Pearson correlation and Bland-Altman analyses were undertaken. Results Within a single platform, measures were highly correlated irrespective of use of Adaptive or Fixed 30/350 and Fixed 75/350 thresholds (R ≥ 0.819, p < 0.000001). The correlation slope for measures of LAP progressively diminished comparing the Adaptive versus Fixed 30/350 and the Fixed 75/350 versus the Fixed 30/350 approaches but bias was small. Between-platform comparisons yielded less optimal results, particularly with respect to measures of LAP and with one platform yielding both very small LAP volumes and very small ranges of volumes. Conclusion Measures of plaque components are highly correlated irrespective of use of Adaptive or Fixed threshold approaches within a given platform. But measures are more affected by the specific proprietary algorithms employed than by specific thresholding options, especially for LAP.
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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 | 0.011 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".