Reproducibility of epicardial fat quantification using non-contrast cardiac CT in an HIV population
Bibliographic record
Abstract
OBJECTIVEEpicardial fat quantification could have prognostic benefit over traditional cardiovascular risk stratification in the HIV population. In general, methods to evaluate reproducibility of epicardial fat have varied. We aim to evaluate the reproducibility of different epicardial fat measurements using non-contrast cardiac CT in HIV+ and HIV- patients.METHODS AND MATERIALSThis is a cross sectional study, nested in the Canadian HIV and Aging Cohort, a large prospective cohort following more than 800 HIV+ and HIV- patients. Consecutive participants with low/intermediate cardiovascular risk were invited to undergo non-contrast cardiac CT. For inter-observer agreement assessment, two observers performed the measurements of epicardial fat volume, area and thickness in all patients, independently of each other. For intra-observer agreement measurement, observer no 2 repeated all measurements in a random subset of 40 patients, u2265 1 month after the first assessment. Agreement was assessed with the intraclass correlation coefficient (ICC).RESULTSA total of 167 HIV+ and 58 HIV- patients underwent cardiac CT. The inter-observer agreement was excellent for epicardial fat volume (ICC 0.75) and area (ICC 0.95) and good for epicardial fat thickness (ICC at a level near the left anterior descending artery (LAD) 0.64, ICC near right coronary artery (RCA) 0.64). The intra-observer agreement was excellent for epicardial fat volume (ICC 0.97), area (ICC 0.99), thickness at the level of the LAD (ICC 0.71) and good for epicardial fat thickness at the level of the RCA (ICC 0.68).CONCLUSIONQuantification of epicardial fat using non-contrast cardiac CT is adequately reproducible for volume and area.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.026 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.028 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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; both teacher heads agree on what is shown here.
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".