Higher Levels of Immunoglobulin E and Thymus and Activation-Regulated Chemokine Are Associated with Conjunctivitis During Lebrikizumab Treatment in Patients with Moderate-to-Severe Atopic Dermatitis
Bibliographic record
Abstract
Abstract: Background: Treatment with anti-interleukin (IL)-13 antibody lebrikizumab is sometimes associated with the occurrence of conjunctivitis in patients with atopic dermatitis (AD). However, predictive factors for its occurrence are unknown. Objective: To identify predictive factors for the occurrence of conjunctivitis during lebrikizumab treatment for AD. Methods: A prospective study was conducted in 140 Japanese patients with moderate-to-severe AD who received lebrikizumab 500 mg at weeks 0 and 2, followed by 250 mg every 2 weeks until week 16, thereafter every 4 weeks for median 24 (ranging 12–36) weeks. We compared baseline clinical and laboratory indices, and background factors between patients who developed conjunctivitis and those who did not. Results: Conjunctivitis occurred in 20 patients (14.3%) during lebrikizumab treatment. Patients with conjunctivitis had significantly higher immunoglobulin E (IgE) and thymus and activation-regulated chemokine (TARC) compared to those without. Logistic regression analysis showed that the occurrence of conjunctivitis was associated with higher IgE (odds ratio [OR]: 1, 95% confidence interval [CI]: 1.0–1.0, P = 0.0287) and TARC (OR: 1, 95% CI: 1.0–1.0, P = 0.0441). Conclusions: Higher IgE and TARC may predict conjunctivitis during lebrikizumab treatment for AD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".