Tocilizumab-Associated Small Bowel Perforation in a Young Patient With Rheumatoid Arthritis: A Lesson to Remember During COVID-19 Pandemic
Bibliographic record
Abstract
Tocilizumab is a recombinant humanized monoclonal antibody directed against the interleukin-6 (IL-6) receptor, which has been used for the treatment of rheumatoid arthritis (RA). A range of side effects have been associated with tocilizumab, with gastrointestinal perforation (GIP) being described as a rare but potentially life-threatening complication that deserves considerable attention. The authors report a case of a young male patient with a history of challenging RA who encountered a lower GIP that was associated with tocilizumab therapy. The occurrence of tocilizumab-induced GIP in this reported patient had initially posed a diagnostic dilemma, as its clinical presentation mimicked other autoimmune inflammatory and infectious diseases that are commonly associated with RA. Physicians should be aware of GIPs as a serious adverse event of tocilizumab use despite being a rare phenomenon, particularly in the era of the global pandemic of coronavirus disease 2019 (COVID-19), when this novel drug has been authorized for the management of selected patients with severe COVID-19 infection. Therefore, early recognition and timely management of GIPs would minimize potential morbidities associated with critically ill COVID-19 patients.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| 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".