Hacking systemic lupus erythematosus (SLE): outcomes of the Waterlupus hackathon
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it