706-P: CGM Disruption: A Common Phenomenon
Bibliographic record
Abstract
Background: Advances in diabetes technology have led to reliance on continuous glucose monitors (CGM) for glycemic management. Insufficient wear time due to device malfunction, insertion problems, displacement or removal for imaging, medical procedures or hospitalization is common. Prescription practices that provide an exact number of sensors without redundancy fail to account for the realities of CGM use. The aim of this study is to characterize CGM disruption rates and consequences. Methods: A RedCap survey was completed by adults from a university based diabetes center with T1D or T2D who utilize CGM. Results: Of 262 surveys sent, 76 were completed. Participants had mean age 54, T1D 69%, female 72%, White 97%, and mean duration of diabetes 28 years. 100% used CGM (85.1% Dexcom, 9.5% Freestyle Libre, 5.4% Medtronic) , 61% insulin pump, and 41% HCL systems. The percent of participants reporting ≥1 CGM disruption events in 1 year: device malfunction (85.1%) , insertion problems (63.0%) , displacement (56.8%) ; removal for imaging (44.6%) , surgery/procedures (13.7%) and hospitalization (4.1%) . Adverse glycemic events attributed to disruption of CGM including hyperglycemia and hypoglycemia occurred ≥1 time in 55.4% and 45.9% of the participants respectively (Figure) . Conclusion: Loss of CGM use prior to the anticipated change date is common. Lack of redundancy of CGM supplies contributes to disruptions in care and adverse glycemic events. Disclosure A.Cedeno: None. P.Krutilova: None. A.Markov: None. J.B.Mcgill: Advisory Panel; Gilead Sciences, Inc., Lilly Diabetes, MannKind Corporation, Novo Nordisk A/S, Provention Bio, Inc., Salix Pharmaceuticals, Consultant; Bayer AG, Boehringer Ingelheim International GmbH, Research Support; Dexcom, Inc., Novo Nordisk. A.M.Mckee: Consultant; Medtronic.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.012 | 0.002 |
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".