Bibliographic record
Abstract
The dual-hormone artificial pancreas is an emerging technology to treat type 1 diabetes. It consists of a glucose sensor, infusion pumps, and a dosing algorithm that directs hormonal delivery. Pre-clinical optimization of dosing algorithms using computer simulations has the potential to accelerate the pace of development for this technology. Current simulation environments are far from complete, and in the following thesis we extend them to include two components: a glucose sensor model that accounts for dropouts of sensor readings, and a glucagon action sub-model. To develop the glucose sensor model, potential drop-outs were augmented to an existing model and their incidences and parameters were estimated simultaneously with the parameters of the model using the Bayesian approach. Drop-outs and model parameters were estimated from data collected from 15 subjects with type 1 diabetes who underwent an artificial pancreas study. Model fitting and parameter estimates were contrasted between the enhanced model and the one-compartment existing model. The enhanced model improves fitting of glucose levels and should allow more realistic simulations. In developing the glucagon action sub-model, we considered eight candidate models of glucagon action featuring a number of possible characteristics: insulin-independent glucagon action, insulin/glucagon ratio effect on hepatic glucose production, insulin-dependent suppression of glucagon action, and the effect of rate of change of glucagon. To assess the models, we used measurements of plasma insulin, plasma glucagon, and endogenous glucose production collected from experiments involving 8 subjects with type 1 diabetes who received four subcutaneous glucagon boluses. We estimated each model's parameters using a Bayesian approach, and the models were contrasted based on the deviance information criterion. The model achieving the best fit features insulin-dependent suppression of glucagon action and incorporates effects of both glucagon levels and its rate of change.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".