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 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 | 0.043 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".