Hacking systemic lupus erythematosus (SLE): outcomes of the Waterlupus hackathon
Bibliographic record
Abstract
INTRODUCTION: There is a growing literature demonstrating the benefits of engaging knowledge-users throughout the research process. We engaged a multi-stakeholder team to undertake a hackathon as part of an integrated knowledge translation (iKT) process to develop nonpharmacological interventions to enhance the economic lives of people with systemic lupus erythematosus (SLE). The aims of this research were to (1) increase understanding of the economic challenges of living with SLE through stakeholder engagement at a research hackathon; (2) investigate possible interventions to improve the economic lives of individuals affected by SLE in Canada; and (3) document the outcomes of the Waterlupus hackathon. METHODS: Waterlupus was held at the University of Waterloo in May 2019, attended by lupus advocacy organization representatives, researchers, physicians, individuals with lived experience and students. We conducted participant observation with participants' understanding and consent; notes from the hackathon were qualitatively analyzed to document hackathon outcomes. RESULTS: At the conclusion of the 28-hour hackathon event, five teams pitched nonpharmacological interventions to address the economic challenges of living with SLE. The winning team's pitch focussed on increasing accessibility of affordable sun-protective clothing. Other Waterlupus outcomes include increased awareness of SLE among participants, and professional and informal networking opportunities. CONCLUSION: This paper contributes to a limited literature on health hackathons. The successful outcomes of Waterlupus emphasize the value of hackathons as an iKT tool. Research about how knowledge-users perceive hackathons is an important next step.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".