The Risk of Microscopic Colitis in Solid-Organ Transplantation Patients: A Population-Based Study
Bibliographic record
Abstract
BACKGROUND: Microscopic colitis (MC) has not been recognized as a complication of transplantation because patients are on immunosuppressant medications. The objective of this work was to describe the risk of developing MC after solid-organ transplantation. METHODS: This population-based cohort study identified all cases of MC diagnosed after kidney, kidney and pancreas, or liver transplantation using pathology and transplantation databases. The annual incidence and point prevalence of MC after transplantation was calculated. The incidence rate of MC among transplantation patients was compared with the general population and presented as a Standardized Incidence Ratio (SIR) with 95% confidence intervals. RESULTS: Seven cases (0.9%) of MC were diagnosed in kidney (n=2), kidney and pancreas (n=1), and liver (n=4) transplantation recipients. The point prevalence of MC was 8.8 per 1000 transplantation recipients. The annual incidence rate of MC in solid-organ transplantation patients was 5.0 cases per 1000 person-years. The SIR of developing MC after transplantation was 50.5 (95% confidence interval 13.6-131.8). The average age of diagnosis of MC was 49.4+/-5.3 years, average time of onset from transplantation was 67.4+/-27.0 months, and the average latency period was 30.1+/-9.0 months. Once diagnosed, all patients responded to MC-specific therapy. CONCLUSION: Physicians should have a low threshold to investigate for MC in solid-organ transplantation recipients who present with chronic diarrhea because this population is at an increased risk of developing MC.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".