Carotid plaque classification: Defining the certainty with which plaque components can be differentiated
Bibliographic record
Abstract
Multicontrast-weighted MRI has the potential to become a powerful tool for assessment of atherosclerotic plaque. However, similarities in MR properties across plaque components limit the certainty with which these components can be differentiated. An understanding of MRI's underlying limitations in distinguishing atherosclerotic plaque components, and optimization of key parameters (including the set of components investigated and contrast weightings used) are required. In this study we analyzed endarterectomy specimens using multicontrast MRI and compared the results with matching histological findings to determine the probability of error, an unbiased measure of the underlying error caused by similarity in the spectral characteristics of components. The total error was >40% when five distinct components were investigated, but this was halved when components with similar functions and intensities were grouped together. When three contrast weightings were used to view plaque, diffusion-weighted imaging (DWI) proved valuable for separating hemorrhage from necrotic core, and "hemorrhage + necrotic" from "loose connective tissue + fibrous tissue." A two-way interaction between contrast weightings and components demonstrated that the value of a contrast can be exploited or marginalized depending on the choice of contrast weightings used.
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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 | 0.021 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".