Cardiovascular Outcomes in the Outpatient Kidney Transplant Clinic
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cardiovascular disease is an important cause of morbidity and death in kidney transplant recipients. This study examines the Framingham risk score's ability to predict cardiac and stroke events. Because cyclosporine and tacrolimus have different cardiovascular risk profiles, these agents were also examined. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: A prospective cohort evaluation of 540 patients were followed for a median of 4.7 yr in an outpatient kidney transplant clinic. Baseline Framingham risk scores were calculated and all cardiovascular outcomes were collected. RESULTS: Rates per 100 patient-years were 1.79 for cardiac and 0.78 for stroke events. The ratio of observed-to-predicted cardiac events was 1.64-fold higher [95% confidence interval (CI) 1.19 to 2.94] for the cohort, 2.74-fold higher (95% CI 1.70 to 4.24) in patients age 45 to 60 with prior cardiac disease or diabetes mellitus, but not higher in other age subgroups. Stroke was not increased above predicted. Risk scores for cardiac (c = 0.65, P = 0.003) and stroke (c = 0.71, P = 0.004) events were modest predictors. 10-yr event scores for cardiac (9.3 versus 13.5%, P < 0.001) and stroke (7.1 versus 10.0%, P = 0.002) were lower for tacrolimus compared with cyclosporine-treated patients. However observed cardiac events were higher in tacrolimus recipients (2.50, 95% CI 1.09 to 5.90) in an adjusted Cox model. CONCLUSIONS: Although risk scores are only modest predictors, patients with the highest event rates are easily identified. Treating high-risk patients with cardioprotective medications should remain a priority.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".