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Record W4411733317 · doi:10.1093/bjd/ljaf085.151

P123 A novel self-referral enrolment pathway in a UK multicentre atopic dermatitis trial (BEACON trial): a single-site prospective observational study to assess feasibility and effectiveness

2025· article· en· W4411733317 on OpenAlexaff
Bindi Gaglani, Sam Curtis, Selina Cox, Petrina Chu, Richard Emsley, Andrew Lovell, Caroline Murphy, Ilona Blee, Richard Woolf, Catherine Smith, Amina Ali, Andrew Pink

Bibliographic record

VenueBritish Journal of Dermatology · 2025
Typearticle
Languageen
FieldMedicine
TopicDermatology and Skin Diseases
Canadian institutionsSt. Thomas Hospital
Fundersnot available
KeywordsObservational studyMedicineAtopic dermatitisReferralPhysical therapyPediatricsDermatologyFamily medicineInternal medicine

Abstract

fetched live from OpenAlex

Abstract Recruiting clinical trial participants to time and target can be challenging. Innovative recruitment strategies, such as facilitating participant self-referral, can be beneficial. The BEACON trial is a national multicentre phase IV assessor-blinded randomized controlled trial comparing the effectiveness, tolerability and cost-effectiveness of ciclosporin, methotrexate and dupilumab in adults with moderate-to-severe eczema. The aim of this study was to describe the impact of self-referral at the sponsor (chief investigator) site in the first year of active recruitment. The BEACON trial opened in November 2023. Self-referral was promoted via the BEACON trial and National Eczema Society websites, social media channels and meetings. Interested participants registered their interest via the BEACON website, then were contacted by their nearest trial site. They were subsequently sent a seven-point questionnaire to assess key eligibility criteria and were contacted via telephone by a subinvestigator (with or without photos) once the questionnaires were completed. If deemed suitable they were invited in for formal screening. Data relating to the mechanism of referral and subsequent progress of all potential participants were prospectively collected and analysed. In total, 594 self-referrals have been received through the trial website, of which 127 were forwarded to the sponsor (chief investigator) site. All 127 referrals were contacted, of whom 66 (52%) were screened, 51 (40%) passed screening and 41 (32%) were randomized to study treatment (similar to internal hospital referrals: 46%, 38% and 32%). Among self-referrals, 61 (48%) did not proceed to screening due to disease severity (31%), being unreachable (25%), comorbidities (15%), prior medications (13%) or other reasons. Of the self-referrals screened (66), 15 (23%) did not pass screening. This was due to low disease severity (60%), change of mind (13%) or undisclosed comorbidities (27%). Of those participants who passed screening (51), 53% were of White ethnicity, 24% Asian, 14% mixed and 8% Black. Self-referral participants had an average Eczema Area and Severity Index score of 30.6 (range 19.8–39.1), including one patient with erythroderma. In conclusion, self-referral has proven to be an important, efficient and highly effective mechanism of recruitment into the BEACON trial. It has been as effective as the more traditional site-based identification and has helped increase accessibility and representation of underserved groups. The high level of interest and disease severity may in part reflect the ongoing access challenges within the National Health Service. These data demonstrate that enabling self-referral can significantly aid clinical trial recruitment.

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 imitation

Not 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.

metaresearch head score (Codex)0.016
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.031
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.002

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.

Opus teacher head0.070
GPT teacher head0.340
Teacher spread0.271 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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