Serum Ustekinumab Concentrations Are Associated With Remission in Crohn’s Disease Defined by a Serum-Based Endoscopic Healing Index
Bibliographic record
Abstract
Abstract Background Optimal ustekinumab levels (UST) in Crohn disease (CD) treatment have not been defined. We set out to define the optimal UST to differentiate between remission and active CD, as defined using the serum-based endoscopic healing index (EHI). Methods Paired serum UST and EHI tests were analyzed. Remission was defined as EHI <20. Active disease was defined as EHI ≥50. The proportion of patients in remission was compared across UST quartiles. UST in subjects with EHI <20 and EHI ≥50 were compared. An area under receiver operating characteristic curve was generated to identify an optimal UST to differentiate between active disease and remission. Results A total of 337 unique patients were identified; median UST and EHI were 5.0 µg/mL [interquartile range (IQR) 2.7–9.1] and 37 (IQR 26–53), respectively. EHI <20 (remission) was found in 57 (16.9%) patients. EHI ≥50 (active disease) was found in 97 (28.8%) patients. Higher proportions of subjects were in remission for increasing UST quartiles, P = 0.01. Median UST in patients with EHI <20 and EHI ≥50 were 7.5 µg/mL (IQR 4.6–10.9) and 3.1 µg/mL (IQR 1.8–6.6), respectively, P < 0.001. An UST threshold of 3.75 µg/mL optimally differentiated between active disease and remission (area under the curve 0.725). UST levels >3.75 µg/mL were associated with a lower proportion of subjects with active disease (EHI ≥50; 18.9%) compared with UST levels ≤3.75 µg/mL (45.6%, P < 0.001). Conclusions Using the EHI, we identified a threshold UST level of 3.75 µg/mL to optimally differentiate between active and quiescent CD. These data suggest that UST serum concentrations of >3.75 µg/mL are optimally associated with endoscopic remission in CD.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".