P3380Longitudinal assessment of coronary atherosclerosis according to baseline and changes of serum hemoglobin level
Bibliographic record
Abstract
Abstract Introduction Little is known regarding the impact of serum hemoglobin level changes (Δ hemoglobin) on coronary plaque volume. This study evaluated the association between Δ hemoglobin and coronary plaque volume change (PVC) using serial coronary computed tomographic angiography (CCTA). Methods A total of 830 subjects (61±10 years, 51.9% male) who underwent serial CCTA with available hemoglobin levels were analyzed from the Progression of AtheRosclerotic PlAque DetermIned by Computed TomoGraphic Angiography IMaging (PARADIGM) registry. The median inter-scan period was 3.2 (2.5 to 4.4) years. Quantitative assessment of coronary plaques was performed at both scans. All participants were stratified into four groups based on the quartile of baseline hemoglobin levels. Annualized PVC was defined as total PVC divided by inter-scan period. Plaque progression (PP) was defined as plaque volume at follow-up minus plaque volume at index >0. Results Baseline total plaque volume (mm3) was not different among all groups (group I [lowest]: 34.1 (0.0–127.4) vs. group II: 28.8 (0.0–123.0) vs. group III: 49.9 (5.6–135.0) vs. group IV [highest]: 34.3 (0.0–130.7); p=0.235). During follow-up, Δ hemoglobin was related to annualized PVC (β:−0.114; p=0.001) and PP (odds ratio: 0.868; 95% confidence interval: 0.770–0.978; p=0.020). Multiple linear regression models showed that Δ hemoglobin significantly impacted on annualized PVC in only the composite of I and II groups. Conclusion Based on serial CCTA findings, Δ hemoglobin independently impacted on coronary PVC in individuals with low to normal baseline hemoglobin level.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".