A review of reported infectious events following rituximab therapy in pemphigus patients
Bibliographic record
Abstract
Pemphigus is a rare autoimmune blistering condition that used to be fatal before the introduction of corticosteroid (CS) and immunosuppressive agents. Rituximab is a monoclonal anti-CD-20 antibody that induces the pathologic B-cells apoptosis with significant efficacy in the treatment of pemphigus. The application of rituximab can lead to infectious events. We aim to review the reported infectious events in pemphigus patients who previously received rituximab and classify them based on the causative agents. A thorough search of PubMed was conducted using the keywords "rituximab," "pemphigus," "infection," "viral disease," "viral infection," "complication," "efficacy" and their combinations also applying their equivalent Mesh terms and including the references cited in each study. All studies that mentioned at least one infectious event were included. A total of 77 infectious events in 68 patients were reported in the literature out of which the most reported causative agent was viral but the most fatal one found to be bacterial. Although rituximab therapy has shown promising results in controlling pemphigus patients mainly the refractory cases, given possible fatal outcomes, we believe the medical profile of the patients before initiating the therapy warrants careful examination to search for any risk factors or predisposing conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".