Efficacy and safety of up to two years of tralokinumab treatment in adults of different racial subgroups with moderate-to-severe atopic dermatitis
Bibliographic record
Abstract
73 81 56 57 61 Responders, % DLQI ≤5 Materials and Methods Patients and treatment • In ECZTEND, patients who completed PT of tralokinumab received open-label tralokinumab 300 mg every two weeks (Q2W, home use) after a 600 mg loading dose plus optional topical corticosteroids (US class ≥4 or Europe class ≤3) or topical calcineurin inhibitor, with visits every 8 weeks o All patients who completed PT at sites with ECZTEND were eligible to enroll in ECZTEND, regardless of prior treatment or response o For key inclusion and exclusion criteria, please see Blauvelt et al 3 • Adult patients included in this post hoc analysis completed 56 weeks of open-label tralokinumab treatment in ECZTEND by the data cutoff (April 30, 2021) (Figure 1A) • Patients self-reported race at baseline of ECZTEND (Figure 1B) • Proportion of patients achieving EASI-75/90, IGA 0/1, EASI≤7, Worst Weekly Pruritus NRS≤ 3, and DLQI ≤ 5 were assessed.EASI-75/90 determined relative to PT baseline • Data are presented as observed.Intermittent missing data are presented using last observation carried forward (LOCF).Modified non-responder imputation (mNRI) sets discontinuation from ECZTEND due to adverse event(s) or lack of efficacy as non-response and uses LOCF for other missing data
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".