Bibliographic record
Abstract
While for some individuals with atopic dermatitis (AD), the disease can be controlled with topical treatments, those with moderate-to-severe AD often require systemic therapy for long-term disease control. Systemic treatments for AD include conventional systemic agents, small molecule inhibitors, and biologics, each with its own risks and benefits. For example, conventional systemic agents carry significant risks with long-term use, and small molecule inhibitors require frequent dosing. Melinda Gooderham, SkiN Centre for Dermatology, Probity Medical Research, Queen’s University, Peterborough, Canada; Marjolein de Bruin-Weller, University Medical Center Utrecht, the Netherlands; and April Armstrong, University of California, Los Angeles, USA, are internationally renowned specialists in AD. Here, they discuss how the advent of biologic therapy for AD has changed clinical practice. One of the more recent biologic therapies to become available is tralokinumab. This is administered as a single injection every 2 or 4 weeks, is well-tolerated, and can be used over the long term without diminishing efficacy. Biologics such as tralokinumab are at the forefront of a change from flare-driven treatment to the management of AD on a stable, long-term basis, with associated improvements in health-related quality of life (HRQoL) for patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".