Comparison of Serum Concentrations of Ustekinumab Obtained by Three Commercial Assays in Patients with Crohn’s Disease
Bibliographic record
Abstract
Abstract Background Data on the association of ustekinumab (UST) drug concentrations and clinical outcomes are conflicting. We assessed serum UST drug and anti-UST antibody concentrations using three commercially available assays. Methods Sixty-one blood samples were analyzed for serum UST drug and anti-UST antibody concentrations using three assays: one homogeneous mobility shift assay (HMSA, Prometheus, Assay A), and two enzyme-linked immunosorbent assays (ELISA; Progenika, Dynacare, Assay B and Theradiag, Assay C). Results The median (IQR) serum UST concentrations for the three assays were: Assay A 7.50 (5.35 to 12.88) µg/mL, Assay B 4.02 (2.46 to 6.95) µg/mL and Assay C 4.35 (2.62 to 7.50) µg/mL. A Kruskal–Wallis test confirmed a statistically significant difference between the different assays, X2(2) = 30.606, p < 0.001. Linear regression showed near twofold increased difference in the absolute drug concentrations between the HMSA and either ELISA. Linear quantitative correlation was observed for all three assays (r = 0.836 for A versus B, r = 0.792 for A versus C, r = 0.936 for B versus C; p < 0.01). The intraclass correlation coefficient (ICC) between assay A and B was 0.649 (95% confidence interval [CI] −0.208 to 0.874); assay A and C was 0.671 (95% CI −0.165 to 0.878); and assay B and C was 0.958 (95% CI 0.928 to 0.975); p < 0.001. No anti-UST antibodies were detected. Conclusion A good correlation was observed for serum UST drug concentrations and a good agreement was observed between the ELISA tests. However, agreement was poor between the HMSA and each ELISA tests. Clinical recommendations regarding drug concentrations should be based on assay type used.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".