Adalimumab drug and antibody levels as predictors of clinical and laboratory response in patients with Crohn's disease
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Bibliographic record
Abstract
BACKGROUND: Adalimumab is an effective treatment for Crohn's disease (CD). Anti-adalimumab antibodies (AAA) and low trough serum drug concentrations have been implicated as pre-disposing factors for treatment failure. AIMS: To assess adalimumab and AAA serum levels, and to examine their association and discriminatory ability with clinical response and serum C-reactive protein (CRP). METHODS: We performed a cross-sectional study using trough sera from adalimumab-treated CD patients. Demographical data, Montreal classification, treatment regimen and clinical status were recorded. Serum adalimumab, AAA and CRP were measured. Receiver operating characteristic analysis and a multivariate regression model were performed to find drug and antibody thresholds for predicting disease activity at time of serum sampling. RESULTS: One hundred and eighteen trough serum samples were included from 71 patients. High adalimumab trough serum concentration was associated with disease remission (Area Under Curve 0.748, P < 0.001). A cut-off drug level of 5.85 μg/mL yielded optimal sensitivity, specificity and positive likelihood ratio for remission prediction (68%, 70.6% and 2.3, respectively). AAA were inversely related with adalimumab drug levels (Spearman's r = -0.411, P < 0.001) and when subdivided into categorical values, positively related with disease activity (P < 0.001). High drug levels and stricturing vs. penetrating or inflammatory phenotype, but not AAA levels, independently predicted disease remission in a multivariate logistic regression model. CONCLUSIONS: Adalimumab drug levels were inversely related to disease activity. High levels of anti-adalimumab antibodies were positively associated with disease activity, but this association was mediated mostly by adalimumab drug levels.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it