User‐driven open‐source artificial pancreas systems and patient‐reported outcomes: A missed opportunity?
Bibliographic record
Abstract
AIM: This paper aims to highlight the attributes of engagement and urgency to act to control diabetes demonstrated by open-source artificial pancreas system users with the view that increased user involvement in research and practice can capitalize on these self-management traits; and to outline the challenges of researching outcomes in the context of unlicensed therapies. METHODS: A group of technically minded people with type 1 diabetes have reverse-engineered commercially available diabetes devices to help them achieve the diabetes outcomes they desire. Although studies have reported improved biomedical outcomes with these artificial pancreas systems, there are only a few studies examining patient-reported outcomes. RESULTS: The investigation of patient-reported outcomes for open-source artificial pancreas system users has been hampered by the rapid advances in the technology, the lack of randomized controlled trials and the ethical challenges of researching unregulated technologies. There is an on-going debate about the most appropriate types of measures to evaluate patient-related outcomes. CONCLUSIONS: The early adopters of open-source artificial pancreas systems exhibit many of the characteristics that predict optimal diabetes outcomes through engagement and urgency regarding self-management. These qualities should be harnessed to improve research in this and other areas of diabetes management.
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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 | 0.013 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".