961-P: Comparison of Glucose Metrics by Capillary Blood Glucose and Continuous Glucose Monitoring among People with Type 2 Diabetes on Hemodialysis
Bibliographic record
Abstract
Background: Reliability of HbA1c in subjects with end-stage kidney disease treated with hemodialysis is poor. Capillary blood glucose (CBG) is widely used, but is limited to 2-4 values/day. Clinical guidelines recommend using continuous glucose monitoring (CGM) to assess glycemic control in this population, but there is limited data. Methods: Prospective observational study of insulin treated adults with type-2 diabetes (T2D) receiving hemodialysis. Subjects were instructed to wear a Dexcom G6-Pro and perform Nova StatStrip CBG at least 2-4/d for 10-days. We compared mean glucose, hypoglycaemia, and hyperglycaemia rates, time-in-target range (TIR), time above range (TAR), and time below range (TBR) between testing methods. Results: Glucose metrics are shown in Table 1. Conclusion: Compared to CBG, CGM provides a comprehensive glycemic evaluation in insulin-treated subjects with T2D on hemodialysis. CGM was superior in assessing glycemic control (TIR), detection of severe hyperglycemia and hypoglycemic excursions, particularly nocturnal and prolonged events. Safer treatment options assessed by CGM metrics are needed for this high-risk population. Disclosure R.J.Galindo: Consultant; Novo Nordisk, Eli Lilly and Company, Sanofi, Pfizer Inc., Bayer Inc., WW (Weight Watchers), Research Support; Novo Nordisk, Eli Lilly and Company, Dexcom, Inc. A.Y.G.Gerges: None. B.Moazzami: None. L.Peng: None. K.R.Tuttle: Consultant; Lilly, AstraZeneca, Gilead Sciences, Inc., Research Support; Bayer Inc., Boehringer Ingelheim (Canada) Ltd., Novo Nordisk, Goldfinch Bio, Inc., Traveere Pharmaceuticals. G.Umpierrez: Research Support; Abbott, Dexcom, Inc., Baxter.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".