Guidelines for the treatment and management of new‐onset diabetes after transplantation<sup>1</sup>
Bibliographic record
Abstract
Although graft and patient survival after solid organ transplantation have improved markedly in recent years, transplant recipients continue to experience an increased prevalence of cardiovascular disease (CVD) compared with the general population. A number of factors are known to impact on the increased risk of CVD in this population, including hypertension, dyslipidemia and diabetes mellitus. Of these factors, new-onset diabetes after transplantation has been identified as one of the most important, being associated with reduced graft function and patient survival, and increased risk of graft loss. In 2003, International Consensus Guidelines on New-onset Diabetes after Transplantation were published, which aimed to establish a precise definition and diagnosis of the condition and recommend management strategies to reduce its occurrence and impact. These updated 2004 guidelines, developed in consultation with the International Diabetes Federation (IDF), extend the recommendations of the previous guidelines and encompass new-onset diabetes after kidney, liver and heart transplantation. It is hoped that adoption of these management approaches pre- and post-transplant will reduce individuals' risk of developing new-onset diabetes after transplantation as well as ameliorating the long-term impact of this serious complication.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.008 |
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".