Bibliographic record
Abstract
Sirs, I read with interest the updated review article by Yen and Pardi1 regarding microscopic – particularly lymphocytic and collagenous – colitis (MC). However, they omitted to quote, in their ‘risk factors/disease associations’ section, an important clinical situation recently suggested to be associated with MC, i.e. solid-organ transplantation.2 In a population-based cohort study of 787 kidney, kidney and pancreas or liver transplantation recipients in Southern Alberta, Canada, were shown a point prevalence of 8.8 cases of MC per 1000 transplantation patients and an annual incidence rate of 5.0 per 1000 transplantation person-years, to be compared with 0.1 per 1000 person-years in the general population; the standardised incidence ratio of developing MC after transplantation was 50.5 (13.6–131.8).2 Diarrhoea affects between 15% and 75% of post-transplant patients3, 4; its causes generally include gastrointestinal infections and direct toxic effects of medications.3 It is of interest that MC might be added to these causes, even if its pathogenesis remains unknown in this setting. Theoretically, MC should not be common in patients with solid-organ transplantation because of the use of immunosuppressant therapy: indeed, corticosteroids and azathioprine often are effective in MC treatment.5 The reported increased MC incidence in solid-organ recipients2 is of special interest owing to (a) the association of MC with autoimmune disease (AID) (up to 40% of MC patients have an AID such as celiac disease, thyroiditis, type 1 diabetes mellitus, rheumatoid arthritis or psoriasis5, 6); (b) the recent discovery of a critical role for loss of self-tolerance and development of IL-17-dependent autoimmunity in solid-organ recipients7; and (c) the cases of de novo ulcerative colitis, Crohn’s disease8, 9 and autoimmune hepatitis10 reported after liver transplantation. Regarding autoimmunity in MC, further studies on Treg cells and T-cell regulation are required. Declaration of personal and funding interests: None.
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.
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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".