Measurement of Plaque Characteristics Using Coronary Computed Tomography Angiography: Achieving High Interobserver Performance
Bibliographic record
Abstract
BACKGROUND: Coronary computed tomography angiography (CCTA) is used to assess plaque characteristics, remodelling, and progression and regression. Few papers address standard operating procedures that ensure achievement of high interobserver reproducibility. Moreover, assessment of coronary artery bypass grafts has not been reported. METHODS: A training set of images was created of native coronary segments, spanning the full range of atheromatous disease from normal to severe, excluding totally occluded segments, and including segments with or without calcification (n = 24) and completely normal-appearing bypass grafts (n = 16). Three observers used a validated software program during a training phase to establish standard operating procedures and then to achieve high intraobserver performance based on Pearson's correlation coefficient. Subsequently, interobserver variability for the laboratory as a whole was determined with a focus on measures of plaque volume, low- attenuation plaque (LAP), mixed plaque (MP), and calcified plaque (CP). RESULTS: We found no substantive differences in analytical issues between grafts and native vessels and emphasize the aggregated data. The range of mean total plaque percent was approximately 55% of total vessel volume with maximal interobserver mean absolute differences of 2% or less. Percent of LAP, MP, and CP demonstrated interobserver standard errors of 1% to 2% and interobserver mean absolute differences of 0% to 1%. Pearson's correlations were all highly significant and ranged from 0.969 to 0.999. CONCLUSIONS: CCTA provides a rich diversity of measures of atherosclerosis in coronary and bypass segments that are highly reproducible with experience and adherence to standard operating procedures.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".