Proactive therapeutic drug monitoring of infliximab: a comparative study of a new point‐of‐care quantitative test with two established <scp>ELISA</scp> assays
Bibliographic record
Abstract
BACKGROUND: Therapeutic drug monitoring is a powerful strategy known to improve the clinical outcomes and to optimise the healthcare resources in the treatment of autoimmune diseases. Currently, most of the methods commercially available for the quantification of infliximab (IFX) are ELISA-based, with a turnaround time of approximately 8 h, and delaying the target dosage adjustment to the following infusion. AIM: To validate the first point-of-care IFX quantification device available in the market - the Quantum Blue Infliximab assay (Buhlmann, Schonenbuch, Switzerland) - by comparing it with two well-established methods. METHODS: The three methods were used to assay the IFX concentration of spiked samples and of the serum of 299 inflammatory bowel diseases (IBD) patients undergoing IFX therapy. RESULTS: The point-of-care assay had an average IFX recovery of 92%, being the most precise among the tested methods. The Intraclass Correlation Coefficients of the point-of-care IFX assay vs. the two ELISA-based established methods were 0.889 and 0.939. Moreover, the accuracy of the point-of-care IFX compared with each of the two reference methods was 77% and 83%, and the kappa statistics revealed a substantial agreement (0.648 and 0.738). CONCLUSIONS: The Quantum Blue IFX assay can successfully replace the commonly used ELISA-based IFX quantification kits. This point-of-care IFX assay is able to deliver the results within 15 min makes it ideal for an immediate target concentration adjusted dosing. Moreover, it is a user-friendly desktop device that does not require specific laboratory facilities or highly specialised personnel.
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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 | 0.014 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".