Improved Glycemia and Quality of Life Among Loop users – Analysis of Real-World Data from a Single Centre (Preprint)
Bibliographic record
Abstract
BACKGROUND Despite being an unapproved method of insulin delivery, increasing numbers of people with type 1 diabetes (T1D) worldwide are choosing to use Loop, a Do-It-Yourself Automated Insulin Delivery system. OBJECTIVE We aimed to assess glycemic outcomes, safety and the perceived impact on quality of life (QOL), in a local Edmonton cohort of known Loop users. METHODS An observational study of adults with T1D using Loop was performed. Assessment of glycemic; HbA1c and time in range (TIR), and safety outcomes; hospital admissions and time below range (TBR), compared six months of Loop with the user’s prior regulatory approved insulin delivery method. QOL outcomes were assessed using INSPIRE, Diabetes Impact and Device Satisfaction (DIDS) measures (with maximum scores of 100, 10 and 10), and semi-structured interviews. RESULTS 24 adults with T1D, 66.7% female, median (IQR) age 33 (28-45), duration of diabetes 22 years (17-32), with duration of Loop 18 months (12-25). With Loop, median (IQR) HbA1c 7.1% (6.5-7.5), 54mmol/mol (48-58) and TIR 76.5% (64.6-81.9); a significant improvement from prior therapy (p=0.001 and p=0.005), with a non-significant reduction in time 3.0-3.9mmol/L (p=0.17) and <3mmol/L (p=0.53). Two episodes of DKA and no severe hypoglycemia occurred in a total of 470 months Loop use. Positive QOL impact was explored in qualitative analysis and additionally demonstrated through median (IQR) INSPIRE 86 (79-95), DI 2.8 (2.1-3.9) and DS 9 (8.2-9.4). CONCLUSIONS This local cohort of people with T1D, demonstrate a beneficial effect of Loop use on both glycemic control and QOL, with no safety concerns highlighted. CLINICALTRIAL N/A
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".