Management of dupilumab-related ocular surface disorders
Bibliographic record
Abstract
Dupilumab-related ocular surface disorders (DROSD) affect patients on dupilumab for atopic dermatitis,1 with rates up to 24% in clinical trials and symptoms emerging – on average – after about 18 weeks.2 While the exact cause is unknown, it may involve immune dysregulation or altered meibomian gland function.3 The expert consensus on managing DROSD by the British Association of Dermatologists (BAD) Dupilumab Ocular Complications Working Group (DOCWG), published in this issue of the BJD,4 serves as the first consensus guidance on the topic. The group consisted of adult and paediatric dermatologists, ophthalmologists with expertise in DROSD, patient representatives and members of the BAD Clinical Standards Unit, who established important terminology and made evidence-based management recommendations. One of the important recommendations is that initiation of dupilumab treatment in patients without severe conjunctival or corneal diseases should generally proceed without waiting for ophthalmological assessment. The DOCWG recommends assessment of existing eye disease by the dermatologist before prescribing dupilumab and review of standard eye care advice, particularly for those with pre-existing ocular surface disease. Although there is limited evidence, the prophylactic use of lid hygiene and preservative-free artificial tears may serve as a low-cost and safe option, and has been shown in some studies to achieve adequate control of DROSD in 76% of patients with mild symptoms.1
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".