Ustekinumab Therapeutic Drug Monitoring—Impact on Clinical Practice: A Multicenter Cross-Sectional Observational Trial
Bibliographic record
Abstract
BACKGROUND AND AIMS: The value of ustekinumab (UST) therapeutic drug monitoring (TDM) in clinical practice remains unclear. This study examined the impact of UST TDM on clinical decision making in patients with Crohn's disease (CD). METHODS: A total of 110 consecutive UST-treated CD patients were enrolled in this multicenter, single-arm cross-sectional study. During a single study visit, clinical decisions, disease characteristics, and serum and fecal samples were obtained. The primary outcome was congruency of the actual and two hypothetical clinical decisions based on provision of UST TDM (with and without fecal calprotectin [FCP]) to participating clinicians. Decisions were compared against those of a review panel. A sub-study retrospectively measured the associations of clinical outcomes at the next follow-up visit with serum UST concentration [UST]. RESULTS: No differences in the pattern of decisions by clinicians were observed before and after provision of UST TDM (P = 1.0) or UST TDM + FCP (P = 0.86). However, 39% (TDM) and 50% (TDM + FCP) of hypothetical decisions differed from the initial decisions. The review panel's decisions differed with the addition of TDM + FCP (P = 0.0006), but not TDM alone (P = 0.16). The sub-study (n = 53) failed to detect an association between therapeutic serum [UST] at the initial study visit and clinical outcomes at the next visit. CONCLUSIONS: In consecutive CD patients treated with UST, the addition of TDM into routine clinical practice did not significantly impact clinical decisions and there was no association between short-term clinical outcomes and serum [UST]. Further studies are warranted before clinicians routinely implement UST TDM into clinical practice.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".