A nationwide 12‐month observatory of automated insulin delivery shows improved glucose control, sustained adoption, and reduced acute severe events
Bibliographic record
Abstract
AIMS: A nationwide observational study was conducted to assess the 12-month effectiveness of AID systems in the routine care of people with Type 1 diabetes (PwT1D). METHODS: All PwT1D, adults, and children, who initiated AID between January 1, 2022, and December 31, 2022, were included across 79 centres. Clinical data, continuous glucose monitoring (CGM) parameters, acute severe events in the last year, and HbA1c levels were collected at AID initiation, and after 3, 6, and 12 months of AID treatment. Median values [interquartile range, IQR] and % PwT1D with acute severe events were reported. The primary outcome was the change in time in range (TIR; 3.9-10 mmol/L) after 1 year with AID. RESULTS: [6.7], diabetes duration 19 years [20]. AID systems were MiniMed 780G in 49.7%, Tandem Control-IQ in 49.3%, others in 1%. After 12 months, TIR increased from 58.0 [21] to 70.1% [14] while HbA1c levels decreased from 7.6 [1.2] to 7.0% [0.8]. Percent PwT1D experiencing severe hypoglycaemia (SH) decreased from 4.1 to 0.9%, and ketoacidosis from 1.2 to 0.6%. All improvements were observed after 3 months, sustained through 12 months, and statistically significant (p < 0.05). Only 2.8% of PwT1D discontinued AID. CONCLUSIONS: Twelve months of AID use in routine care improved glucose control in PwT1D, among whom there was less experienced SH and a minor discontinuation.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".