A156 SERUM TUMOUR NECROSIS FACTOR-α ANTAGONIST DRUG CONCENTRATIONS IN PATIENTS WITH PYODERMA GANGRENOSUM ASSOSCIATED WITH INFLAMMATORY BOWEL DISEASE
Bibliographic record
Abstract
Abstract Background The utility of therapeutic drug monitoring for guiding the dosing of tumor necrosis factor-α antagonists (TNFAs) in luminal inflammatory bowel disease (IBD) is well-established and well-accepted. TNFAs, specifically infliximab and adalimumab, have become integral to the management of the rare, neutrophilic dermatosis, pyoderma gangrenosum (PG) in IBD. Little is known regarding the target serum TNFA concentrations to guide dosing to achieve resolution of PG in IBD. Aims To describe the serum TNFA concentrations (infliximab or adalimumab) associated with the resolution of PG lesions in patients with IBD. Methods Patients with IBD and associated PG treated with one of infliximab or adalimumab (collectively known as TNFAs) seen at two academic hospitals affiliated with Western University were identified. Serum TNFA concentrations were assessed at the time of PG treatment. Results Nine patients were identified. All patients had IBD-associated PG. Seven patients were treated with infliximab and 2 patients were treated with adalimumab. All patients received standard dosing. Eight patients had complete resolution of their PG, while one had near complete resolution at the time of last follow-up. A median serum infliximab concentration of 3.00 (IQR, 3.52) µg/ml at week 14 and a median serum adalimumab concentration of 2.02 (IQR, 0.98) µg/ml at week 12 were seen at the time of PG treatment. Conclusions Herein, we report low serum TNFA concentrations despite PG healing in a cohort of IBD patients. This is lower than what is in patients for successful TNFA treatment in luminal and fistulising IBD. Funding Agencies NoneNone.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".