Association of COVID-19 With Risk of Posttransplant Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Posttransplant diabetes mellitus (PTDM) is an important complication for solid organ transplant recipients (SOTRs). COVID-19 has been associated with an increased risk of incident diabetes in the general population. However, the association between COVID-19 and new-onset PTDM has not been explored. METHODS: Using the National COVID Cohort Collaborative Enclave, we conducted a cohort study of adults without diabetes receiving a solid organ transplant (heart, lung, kidney, or liver) in the United States between April 1, 2020, and March 31, 2023, with and without a first diagnosis of COVID-19 (COVID + versus COVID - ) within 180 d of SOT. We propensity score matched a single COVID + SOTR with a COVID - SOTR who was diabetes free at the same point posttransplant. Within this matched cohort, we used multivariable Cox proportional hazards models to examine the adjusted risk of PTDM associated with COVID + . RESULTS: Among 1342 COVID + SOTRs matched to 1342 COVID - SOTRs, the crude rate of newly diagnosed PTDM in the 2 y post-COVID was 17% in those with versus 13% in those without COVID-19 ( P = 0.007). COVID-19 was significantly associated with new PTDM (adjusted hazard ratio, 1.37; 95% confidence interval, 1.12-1.68 at 2 y). CONCLUSIONS: Similar to other viral infections, COVID-19 is associated with an increased risk of PTDM in SOTRs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".