Posttransplant Diabetes Mellitus and Immunosuppression Selection in Older and Obese Kidney Recipients
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
RATIONALE & OBJECTIVE: Posttransplant diabetes mellitus (DM) after kidney transplantation increases morbidity and mortality, particularly in older and obese recipients. We aimed to examine the impact of immunosuppression selection on the risk of posttransplant DM among both older and obese kidney transplant recipients. STUDY DESIGN: Retrospective database study. SETTING & PARTICIPANTS: Kidney-only transplant recipients aged ≥18 years from 2005 to 2016 in the United States from US Renal Data System records, which integrate Organ Procurement and Transplantation Network/United Network for Organ Sharing records with Medicare billing claims. EXPOSURES: Various immunosuppression regimens in the first 3 months after transplant. OUTCOMES: Development of DM >3 months-to-1 year posttransplant. ANALYTICAL APPROACH: We used multivariable Cox regression to compare the incidence of posttransplant DM by immunosuppression regimen with the reference regimen of thymoglobulin (TMG) or alemtuzumab (ALEM) with tacrolimus + mycophenolic acid + prednisone using inverse propensity weighting. RESULTS: (aHR, 0.63; 95% CI, 0.46-0.87). LIMITATIONS: Retrospective study and lacked data on immunosuppression levels. CONCLUSIONS: The beneficial impact of steroid avoidance using tacrolimus on posttransplant DM appears to differ by patient age and induction regimen.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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 it