New efforts to incorporate patient-reported outcomes into lupus clinical trials: report of the community meeting convened by the Lupus Accelerating Breakthroughs Consortium (Lupus ABC), 16–17 October 2024
Bibliographic record
Abstract
The Lupus Research Alliance formed the Lupus Accelerating Breakthroughs Consortium (Lupus ABC) in 2023 as a public-private partnership with investigators, pharmaceutical companies, the Food and Drug Administration, National Institutes of Health, patient-focused non-profits and professional societies with the goals of developing initiatives to accelerate lupus drug development in a precompetitive setting, and ensuring that perspectives of people with lived experience of lupus are included into the drug development process. Patient-reported outcome (PRO) measures are a top area of focus for the Lupus ABC. To support future work in the area of PROs, a public meeting was held on 16-17 October 2024 with the objectives of (1) assessing the current state of PROs used in lupus clinical trials, (2) developing a roadmap to advance the use of PROs in lupus clinical trials and (3) identifying and setting priorities for efforts to be pursued by a Lupus ABC PRO Working Group. After a series of presentations, small groups addressed what PRO domains to measure, the questionnaire burden for trial participants, how PROs should be selected for trials and what efforts are needed to advance use of PROs in lupus clinical trials.Meeting participants concluded that although PROs are being collected in most lupus clinical trials, they may be underused as primary or secondary/exploratory endpoints. A strong desire to change the focus in trials from disease activity and signs to include PROs, as a way of highlighting patients' voices, was noted. The group concluded that the specific steps needed are to identify PRO measures appropriate for the target populations of interest, incorporate PROs into endpoints for regulatory decision-making, ensure PRO data are publicly available and accessible to researchers and involve people living with lupus in selection of PRO domains, measures and trial design. For these actions, collaborations among industry, researchers, regulators, payors and patients are needed.
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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 | 0.539 | 0.448 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.008 | 0.022 |
| Research integrity | 0.033 | 0.035 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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.
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".