Taking the bull by the horn: the frontline use of infliximab for the treatment of immune checkpoint inhibitor-induced enterocolitis
Bibliographic record
Abstract
Immune checkpoint inhibitors which activate the host's immune system to fight cancer have brought dramatic improvements to the overall survival of a growing number of deadly malignancies. Their use comes at the expense of often serious immune-related adverse events which consist of an off-target attack of the immune system on potentially any of the human body's healthy organs. For lack of better-validated evidence, and regardless of the organ affected, clinicians often use the same immunosuppressive regimens consisting of high dose corticosteroids followed by the introduction of biologic agents such as the tumor-necrosis alpha inhibitor infliximab for corticosteroid-refractory toxicities. The article by Johnson et al. is timely in providing a more personalized approach for the management of immune-related toxicities affecting the lower digestive tract with many positive clinical outcomes associated with the upfront use of infliximab in association with corticosteroids. This commentary will provide a narrative summary of their findings in light of the current clinical knowledge relevant to the understanding of immune-related enterocolitis.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.017 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".