971-P: "Take a Chance on Me"—A Trial of Automated Insulin Delivery in Those Who Historically Declined Pump Therapy
Bibliographic record
Abstract
Introduction & Objective: In Ontario, Canada there are strict guidelines for those with type 1 diabetes (T1D) to qualify for insulin pump therapy. This often excludes those with significant glycemic variability, mixed adherence and difficulties with carbohydrate counting. However, with automated insulin delivery (AID), there are less managerial steps, making insulin-pump management easier, with improved results given algorithmic insulin modifications. Given this, this historically ignored population, would be most ideal for AID access. Methods: This qualitative and quantitative trial identified four patients with historically difficult to manage diabetes and placed them on a nine-week trial of Dexcom G6 sensor, and six weeks on Tandem Control-IQ. They were given a pre- and post-survey and followed every 2 weeks to evaluate time in range (TIR), hypoglycemia, and assess quality of life. Results: There were N=4 patients, 2 male, duration of diabetes 2-20 years, HbA1c (9.5-11%), pre-trial TIR 20-50%, pre-trial hypoglycemia <1%. Post-trial, A1c (7.3-8.6%), end-trial TIR 46-67%, and post-trial hypoglycemia <2%. Post-survey, comments included “Having the ability to feel more ‘normal’ having access to my sugars all the time without having to finger prick or scan⋯” “I didn’t have to think too much other than carb counting⋯” “Being able to have insulin all the time without giving a needle is great.” Conclusion: This study demonstrates that the advancement in AID should enable a policy change to allow ubiquitous usage to those with T1D for best standard of care. Disclosure C. Ibrahim: None.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".