Efficacy of Adalimumab in Korean Patients with Crohn's Disease
Bibliographic record
Abstract
BACKGROUND/AIMS: Adalimumab is effective for both remission induction and the maintenance of Crohn's disease (CD) in Western countries. We evaluated the efficacy of adalimumab in the conventional step-up treatment approach for CD in Korea. METHODS: We retrospectively reviewed 62 patients with CD who were treated with adalimumab. Their Crohn's disease activity index (CDAI) was measured at weeks 4, 8, and 52. Clinical remission was defined as a CDAI score <150. Induction and maintenance outcomes were analyzed. RESULTS: Forty-one patients (66.1%) achieved a reduction of 70 CDAI points at week 8. Among them, 28 (45.2%) achieved clinical remission at week 8, 20 (32.3%) maintained remission at week 52. The absence of prior anti-tumor necrosis factor (TNF) therapy and Montreal classification L1 at baseline predicted clinical remission at week 8 in the multivariate logistic regression analysis. In the Cox proportional hazards model, the hazard ratio for the secondary loss of response during maintenance therapy after clinical remission induction was significantly higher in patients who showed initial mild CDAI severity or Montreal classification A3. CONCLUSIONS: In our study, anti-TNF therapy-naive and Montreal classification L1 were associated with adalimumab efficacy as induction therapy in CD. Further studies are warranted to determine the prognostic factors for the long-term response after adalimumab therapy.
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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 | 0.001 |
| 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) | 0.001 | 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".