B cell depletion for autoimmune liver diseases: A retrospective review of indications and outcomes
Bibliographic record
Abstract
Abstract Objectives Pediatric autoimmune hepatitis has an incidence of 0.23/100.000 children in North America, with a bleak prognosis if left untreated. Steroids are the therapy of choice but are not always effective. B cell depletion is a safe and effective therapy that allows for a steroid‐sparing protocol, especially in patients who do not tolerate side effects. Methods We retrospectively reviewed rituximab‐treated patients between 2017 and 2022. Demographics, previous treatments, reasons for B cell depletion, response, and adverse effects were noted. Results Six patients with a mean age of 10.2 years were included. All patients had comorbidities that rendered treatment with steroids unsuccessful or undesirable. Rituximab was started at a mean follow‐up of 8 months. After 6 months, the mean alanine transaminase and aspartate transaminase levels decreased from 575 IU/L and 342 IU/L, respectively, to 28 IU/L ( p = 0.02) and 36 IU/L ( p = 0.008), respectively. Mean γ‐glutamyl transpeptidase decreased from 105 to 25 IU/L ( p = 0.01). Immunoglobulin G levels were normalized in all patients ( p = 0.01). No severe adverse events were observed. One patient had persistent hypogammaglobulinemia, and another had lymphopenia. Conclusion B‐cell depletion is an effective and safe treatment for autoimmune liver diseases and should be included as an option, particularly for relapsing patients in whom steroids are undesirable or have shown nonadherence.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".