Cardiovascular Disease Reduction in the Outpatient Kidney Transplant Clinic
Bibliographic record
Abstract
Cardiovascular disease (CVD) is an important cause of death in kidney transplant recipients. Future CVD mortality was estimated by a risk calculator in all patients (n = 439) with a functioning transplant (>6 months), followed at our center. In addition to CURRENT mortality rates, an OPTIMAL rate (adding anti-hypertensive and lipid-lowering therapy in uncontrolled patients) and an HISTORIC rate (higher systolic blood pressures and the absence of statin use in our population 5 years ago) were also calculated. Overall, the predicted CURRENT CVD mortality rates are 0.82 (95% CI 0.81-0.83) of HISTORIC rates. Predicted OPTIMAL CVD mortality rates are 0.90 (95% CI 0.87-0.92) of CURRENT rates. To achieve OPTIMAL rates, a 27% increase in blood pressure and lipid-lowering drug use is required. There were few contraindications to these medications, implying that physician prescribing was the major barrier to minimizing risk. Despite OPTIMAL rates, the transplant population's relative risk is 2.3 (median, 95% CI 2.1-2.5) times higher than that in the general population. Therefore, targeted therapy to reduce CVD risk can have substantial benefit, but CVD mortality may continue to exceed that in the general population.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".