Bibliographic record
Abstract
The risk of hypoglycaemia in individuals with type 1 diabetes is high: about 12% of patients experience severe hypoglycaemia with loss of consciousness or seizure per year. 1 The recent improvements in glucose sensor accuracy, which measure interstitial glucose in real-time, have opened up the opportunity to substantially reduce hypoglycaemia and potentially eliminate mortality related to severe hypoglycaemia. These advances in glucose-sensor technology have led to the development of many systems, commonly termed artifi cial pancreas systems, that automatically dose insulin on the basis of sensed glucose concentrations. The MiniMed 530G system (Medtronic, Northridge, USA), which suspends insulin delivery when the sensed glucose concentration is lower than a set threshold, is already approved and in use in the USA. In The Lancet Diabetes & Endocrinology, Ahmad Haidar and colleagues 2 describe the use of glucagon delivery combined with insulin delivery in an artifi cial pancreas system to reduce hypoglycaemia in children and adolescents with type 1 diabetes at a Canadian summer camp. Glucagon works quickly to raise circulating glucose concentrations by stimulating the breakdown of hepatic glycogen. It is currently approved only as a rescue treatment for severe hypoglycaemia. The approach of combined automated insulin and glucagon delivery has proven successful to prevent and treat hypoglycaemia by our group 3,4 and others. 5–7 By contrast, other research groups have used systems that rely solely on automated insulin delivery, with algorithms that suspend insulin delivery when hypoglycaemia occurs or is predicted to occur. 8 An insulin-only approach is limited by the slow onset and off set of insulin when delivered subcutaneously. Even rapid-acting insulin analogues have clinically signifi cant glucose-lowering activity more than 4 hours after delivery. 9 Study results using insulin-only systems have been generally favourable, but the rates of hypoglycaemia were typically low even in the control condition (conventional pump therapy), indicating these studies were not an ideal assessment of the risk for hypoglycaemia. 10–12
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".