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Record W4404455267 · doi:10.25251/skin.8.supp.475

Bimekizumab impact on draining tunnels: A dynamic assessment in patients with moderate to severe HS using pooled Week 48 results from BE HEARD I&II

2024· article· en· W4404455267 on OpenAlexaff
Thrasyvoulos Tzellos, Jennifer L. Hsiao, Martina L. Porter, Farida Benhadou, Falk G. Bechara, Melinda Gooderham, Hidetoshi Takahashi, Christos C. Zouboulis, Ingrid Pansar, Robert Rolleri, Nicola Tilt, Christopher J. Sayed

Bibliographic record

VenueSKIN The Journal of Cutaneous Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicInterstitial Lung Diseases and Idiopathic Pulmonary Fibrosis
Canadian institutionsProbity Medical ResearchQueen's University
FundersIdorsia PharmaceuticalsLEO PharmaIncytePfizerAstraZenecaEli Lilly and CompanyBristol-Myers Squibb
KeywordsPooled analysisMedicineInternal medicineMeta-analysis

Abstract

fetched live from OpenAlex

Introduction: Hidradenitis suppurativa (HS) is a recurrent, inflammatory skin disease characterized by painful skin lesions in the folds of the skin and deep, dermal abscesses that join to form chronically draining tunnels (DTs).1,2 Bimekizumab (BKZ) is a humanized IgG1 monoclonal antibody that selectively inhibits IL-17F in addition to IL-17A, both abundant in lesional skin.3,4 Here, we assess the effect of BKZ on DT outcomes over 48 weeks (wks) in BE HEARD I&II.5 Procedure/Study: Pooled data included an initial (Wks0–16) and maintenance (Wks16–48) treatment period. Randomization: 2:2:2:1 (initial/maintenance) to BKZ 320mg every 2 wks (Q2W)/Q2W, BKZ Q2W/Q4W, BKZ Q4W/Q4W or placebo (PBO)/BKZ Q2W. Proportions of patients with ≥1/≥3 DTs at baseline achieving 0, 1–2, 3–5 or >5 DTs were analyzed to Wk48 (observed case). Results: Overall, 1,014 patients were randomized to BKZ Q2W/Q2W (N=288), BKZ Q2W/Q4W (N=292), BKZ Q4W/Q4W (N=288) or PBO/BKZ Q2W (N=146). At baseline, in patients with ≥1 DT, proportions with 1–2 DTs ranged from 34.2–37.4%; 3–5 DTs: 27.0–36.9%; >5 DTs: 28.8–35.5%. In patients with ≥3 DTs, proportions with 3–5 DTs ranged from 43.2–56.1%; >5 DTs: 43.9–56.8%. At Wk16, higher proportions of BKZ-treated patients with ≥1 DT at baseline achieved 0 DTs vs PBO: BKZ Q2W/Q2W, 35.4%; BKZ Q2W/Q4W, 35.4%; BKZ Q4W/Q4W, 33.1% vs PBO, 24.7%. At Wk48, the proportions of patients receiving continuous BKZ that achieved 0 DTs increased to: 45.5%, 48.8%, and 46.8% respectively; PBO/BKZ Q2W switchers showed similar trends (46.3%). For patients with ≥3 DTs at baseline, at Wk16 a higher proportion receiving BKZ achieved 0 DTs vs PBO: BKZ Q2W/Q2W, 24.6%; BKZ Q2W/Q4W, 26.8%; BKZ Q4W/Q4W, 21.7%; vs PBO, 11.5%. At Wk48, the proportions receiving continuous BKZ that achieved 0 DTs increased to 35.0%, 42.2%, and 36.0%, respectively. PBO/BKZ Q2W switchers achieved similar levels (38.0%), with a more favorable increase from Wk16 to Wk48 than PBO/BKZ Q2W switchers with ≥1 DT at baseline.Conclusion: At Wk16, a higher proportion of BKZ-treated vs PBO patients with ≥1/≥3 DTs at baseline achieved 0 or 1–2 DTs; proportions increased to Wk48 regardless of treatment arm.

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)0.007
metaresearch head score (Gemma)0.006
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 score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.017
GPT teacher head0.309
Teacher spread0.293 · 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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