960-P: Hemodialysis-Related Glycemic Patterns among People with Insulin-Treated Type 2 (T2D) Diabetes—Role of Continuous Glucose Monitoring (CGM)
Bibliographic record
Abstract
Background: Hemodialysis (HD) impacts glucose and insulin metabolism and increases hypoglycaemia rates. Glucose-containing dialysate decreases the risk, but studies using CGM metrics are lacking. Methods: Prospective observational study of insulin-treated adults with T2D, receiving hemodialysis (dialysate glucose 100mg/dl), at least thrice weekly. Subjects were instructed to wear a Dexcom G6-Pro for 10 days. We assessed CGM metrics (mean glucose, %TIR, %TAR, %TBR, and hypoglycemic and hyperglycemic rates) during three time periods: 20-hours before (PreHD), during (HD, ~4 hours), and 20-hours after (PostHD) hemodialysis sessions. Results: Among 56 subjects (mean age 57.3±9, HbA1c 7.2±1.4), mean glucose, %TAR, %TIR and rates of hypoglycaemia <70mg/dl were significantly better during HD. %TAR >180mg/dl and > 250mg/dl were significantly higher after HD, see Table 1. Other hypoglycaemia metrics were overall low, and not different in relation to HD timing. Conclusion: CGM patterns demonstrated improved glycemic control during dialysis, with tendency for higher glucose metrics before HD. Hypoglycaemia metrics were low, while severe hyperglycemia excursions are common across all HD-related periods. Future studies using newer CGM technology and providing longer duration of CGM monitoring are needed for subjects with T2D treated by HD. 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. B.Moazzami: None. A.Y.G.Gerges: 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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".