The ALPHA Project: Establishing consensus and prioritisation of global community recommendations to address major challenges in lupus diagnosis, care, treatment and research
Bibliographic record
Abstract
The Addressing Lupus Pillars for Health Advancement (ALPHA) Project is a global consensus effort to identify, prioritise and address top barriers in lupus impacting diagnosis, care, treatment and research. To conduct this process, the ALPHA Project convened a multistakeholder Global Advisory Committee (GAC) of lupus experts and collected input from global audiences, including patients. In phase I, the ALPHA Project used expert interviews and a global survey of lupus experts to identify and categorise barriers into three overarching pillars: drug development, clinical care and access to care. In phase II, reported here, the GAC developed recommended actionable solutions to address these previously identified barriers through an in-person stakeholder meeting, followed by a two-round scoring process. Recommendations were assessed for feasibility, impact and timeline for implementation (FIT), where potential FIT component values were between 1 and 3 and total scores were between 3 and 9. Higher scores represented higher achievability based on the composite of the three criteria. Simplifying and standardising outcomes measures, including steroid sparing as an outcome (drug development) and defining the lupus spectrum (clinical care) ranked as the highest two priority solutions during the GAC meeting and received high FIT scores (7.67 and 7.44, respectively). Leveraging social media (access to care) received the highest FIT score across all pillars (7.86). Cross-cutting themes of many solutions include leveraging digital technology and applying specific considerations for special populations, including paediatrics. Implementing the recommendations to address key barriers to drug development, clinical care and access to care is essential to improving the quality of life of adults and children with lupus. Multistakeholder collaboration and guidance across existing efforts globally is warranted.
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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 | 0.443 | 0.407 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.019 | 0.012 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.012 | 0.043 |
| Research integrity | 0.008 | 0.014 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".