Validation of a screening protocol for identifying low‐risk candidates with type 1 diabetes mellitus for kidney with or without pancreas transplantation
Bibliographic record
Abstract
BACKGROUND: Certain clinical risk factors are associated with significant coronary artery disease in kidney transplant candidates with diabetes mellitus. We sought to validate the use of a clinical algorithm in predicting post-transplantation mortality in patients with type 1 diabetes. We also examined the prevalence of significant coronary lesions in high-risk transplant candidates. METHODS: All patients with type 1 diabetes evaluated between 1991 and 2001 for kidney with/without pancreas transplantation were classified as high-risk based on the presence of any of the following risk factors: age >or=45 yr, smoking history >or=5 pack years, diabetes duration >or=25 yr or any ST-T segment abnormalities on electrocardiogram. Remaining patients were considered low risk. All high-risk candidates were advised to undergo coronary angiography. The primary outcome of interest was all-cause mortality post-transplantation. RESULTS: Eighty-four high-risk and 42 low-risk patients were identified. Significant coronary artery stenosis was detected in 31 high-risk candidates. Mean arterial pressure was a significant predictor of coronary stenosis (odds ratio 1.68; 95% confidence interval 1.14-2.46), adjusted for age, sex and duration of diabetes. In 75 candidates who underwent transplantation with median follow-up of 47 months, the use of clinical risk factors predicted all eight deaths. No deaths occurred in low-risk patients. A significant mortality difference was noted between the two risk groups (p = 0.03). CONCLUSIONS: This clinical algorithm can identify patients with type 1 diabetes at risk for mortality after kidney with/without pancreas transplant. Patients without clinical risk factors can safely undergo transplantation without further cardiac evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".