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Record W4320730827 · doi:10.1136/lupus-2023-000901

Meeting report: the ALPHA project: a stakeholder meeting on lupus clinical trial outcome measures and the patient perspective

2023· article· en· W4320730827 on OpenAlexaff
Joy Buie, Lauren Bloch, Eric F. Morand, Ronald van Vollenhoven, Victoria P. Werth, Zahi Touma, Peter E. Lipsky, Kenneth Kalunian, Anca Askanase, Luís Inês, Christopher A. Reed, MaryBeth Son, Timothy R. Franson, Karen H. Costenbader, Laura E. Schanberg

Bibliographic record

VenueLupus Science & Medicine · 2023
Typearticle
Languageen
FieldMedicine
TopicSystemic Lupus Erythematosus Research
Canadian institutionsUniversity of TorontoUniversity Health Network
FundersEMD SeronoGlaxoSmithKlineAstraZeneca
KeywordsMedicineSystemic lupus erythematosusClinical trialDrug developmentStakeholder engagementAlternative medicineStakeholderDiseaseIntensive care medicineFamily medicineDrugPublic relationsPathologyPsychiatry

Abstract

fetched live from OpenAlex

Drug development in lupus has improved over the past 10 years but still lags behind that of other rheumatic disease areas. Assessment of prospective lupus therapies in clinical trials has proved challenging for reasons that are multifactorial including the heterogeneity of the disease, study design limitations and a lack of validated biomarkers which greatly impacts regulatory decision-making. Moreover, most composite outcome measures currently used in trials do not include patient-reported outcomes. Given these factors, the Addressing Lupus Pillars for Health Advancement Global Advisory Committee members who serve on the drug development team identified an opportunity to convene a meeting to facilitate information sharing on completed and existing outcome measure development efforts. This meeting report highlights information presented during the meeting as well as a discussion on how the lupus community may work together with regulatory agencies to simplify and standardise outcome measures to accelerate development of lupus therapeutics.

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 imitation

Not 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.

metaresearch head score (Codex)0.033
metaresearch head score (Gemma)0.048
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0330.048
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.006
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.221
GPT teacher head0.441
Teacher spread0.220 · 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; both teacher heads agree on what is shown here.

Study designQualitative
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

Citations3
Published2023
Admission routes1
Has abstractyes

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