Evidence of enhanced systemic inflammation in stable kidney transplant recipients with low Framingham risk scores
Bibliographic record
Abstract
BACKGROUND: While the Framingham risk score (FRS) predicts cardiovascular risk in the general population, it underestimates cardiovascular events in renal transplant recipients (RTR). Inflammation is common in RTR, and it is also a hallmark of vascular injury contributing to cardiovascular events. OBJECTIVE: To explore the relationship between inflammatory chemokines (CCL family) and FRS in a stable RTR. METHODS: The modified FRS (2009) was used to calculate the 10-yr probability of CVE in 150 RTR. A cross-sectional study measured plasma levels of 14 CCLs by Luminex technique in 53% (79/150) of the cohort and 28 controls. RESULTS: 43.3% of RTR was classified as low, 16% moderate, and 40.7% high FRS. FRS correlated with eGFR and all CCLs with R of <0.2(p = n.s). Compared with controls, CCL 1,4,8,15, and 27 were equally increased in both the high and low FRS groups (p < 0.04 and 0.03, respectively). The percentage of patients with low FRS and CCL 8,15, and 27 values above the 95% cutoff control levels was 46.1%, 76.9%, and 53.8%, respectively. CONCLUSIONS: Over one half of stable RTR, including those with low FRS, have increased inflammatory chemokine levels. Inflammation is not accounted for in the FRS, and this may explain the poor performance of FRS in transplant patients.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".