Can We Really Close the Loop and How Soon? Accelerating the Availability of an Artificial Pancreas: A Roadmap to Better Diabetes Outcomes
Bibliographic record
Abstract
Development of a closed-loop artificial pancreas has been a long-time goal that could transform diabetes management. The primary limitation until recent years was the lack of a robust and portable continuous glucose sensor. There has been significant progress over the past 5 years in the development and commercialization of continuous glucose monitoring (CGM) devices. Used adjunctively, CGM has been demonstrated to add significant value in improving diabetes management by increasing time spent in glycemic targets and improving overall glycemic control. However, these benefits are limited by the human user's finite capacity to respond to the data provided by the device. By automating even a portion of the insulin delivery functionality of combined sensor/pump systems via computer algorithm, impending excursions could be handled more quickly and effectively. This review will describe very promising preliminary closed-loop studies, describe a potential roadmap to an artificial pancreas that will be safe and effective, and propose a solution-a hypo- and hyperglycemia minimizing control-to-range approach-that may allow for near-term delivery of a semiautomated system to people with diabetes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".