Patients With Low Drug Levels or Antibodies to a Prior Anti–Tumor Necrosis Factor Are More Likely to Develop Antibodies to a Subsequent Anti–Tumor Necrosis Factor
Bibliographic record
Abstract
Therapeutic drug monitoring (TDM) with measurement of serum drug and antidrug antibodies (ADAb) is used widely to confirm therapeutic exposure, rule out immunogenicity, and optimize treatment of biologics in patients with inflammatory bowel diseases.1 A recent genome-wide association study found the variant HLA-DQA1∗05 to increase the risk of development of antibodies against infliximab (IFX) and adalimumab (ADM) 2-fold, regardless of concomitant immunomodulator use.2,3 However, there is currently limited evidence showing whether patients who develop antibodies to 1 anti–tumor necrosis factor (TNF) are prone to develop antibodies to the subsequent anti-TNF. Our aim was to investigate the risk of subsequent antibody development in cases (with ADAb to prior anti-TNF) versus control subjects (without ADAb to prior anti-TNF) using a large cohort of patients with inflammatory bowel diseases who underwent TDM with a drug-tolerant assay.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".