The Role of Inflammation and High-Sensitivity C-Reactive Protein in Atherosclerotic Cardiovascular Disease and Chronic Kidney Disease: The FLAME-ASCVD Survey among Nephrologists
Bibliographic record
Abstract
BACKGROUND: Systemic inflammation (SI) contributes to increased cardiovascular risk in patients with atherosclerotic cardiovascular disease (ASCVD) and chronic kidney disease (CKD). We assessed clinical perceptions toward SI and usage of high-sensitivity C-reactive protein (hsCRP) among nephrologists. METHODS: FLAME-ASCVD Nephro was an online survey of nephrologists from 10 countries who treat ≥20 patients with ASCVD and CKD a month and were practicing for ≥3 years. Results were analyzed using descriptive statistics. RESULTS: Of 513 nephrologists who responded, 300 completed and were included in the survey; the mean age was 46 years and the mean time in practice was 16 years. Hypertension (89%), overweight/obesity (81%), and CKD (80%) were the ASCVD risk factors most often discussed with patients (SI was ninth). The most common unmet needs (ranked 1-3) for patients with ASCVD and CKD were "lack of effective SI treatment options" (44%), "limited awareness of the role of SI in ASCVD" (35%), and "higher risk of CV events" (33%). Seventy-four percent of nephrologists wanted to learn more about the role of SI in ASCVD and 71% test for and use SI results when determining management approaches. Seventy percent of nephrologists considered hsCRP testing in patients with ASCVD and CKD (aided), and proven clinical efficacy of hsCRP was the top reason (37%); out-of-pocket cost (30%) was the most common reason for not considering hsCRP testing. CONCLUSION: Lack of effective treatment options for SI remains the most common unmet need for patients with ASCVD and CKD. Further medical education is needed to raise awareness among nephrologists about the role of SI and hsCRP testing.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".