High Hemoglobin <scp>A1c</scp> levels and glycemic variability increase risk of severe hypoglycemia in diabetic hemodialysis patients
Bibliographic record
Abstract
While hyperglycemia is central to the pathogenesis and management of diabetes mellitus, hypoglycemia and glucose variability also contribute to outcomes. We previously reported on the relationship of glycemic control to outcomes in a large population of diabetic end-stage renal disease (ESRD) patients. Recognizing that ESRD is a risk factor for severe hypoglycemia, we have now analyzed the association between glycosylated hemoglobin A1c (HgbA1c) levels and glycemic variability in those with hypoglycemia. This is a retrospective study of patients with diabetes enrolled in a large hemodialysis program. Hypoglycemia was identified from hospital discharge diagnostic codes. Glycemic variability was assessed by the standard deviation of HgbA1c and glucose levels over time. Hypoglycemia as a discharge diagnosis was documented in 4.1% of patients. Higher baseline HgbA1c was associated with greater risk for hypoglycemia hospitalization, a finding confirmed by time-lagged HgbA1c levels drawn a quarter earlier. Higher baseline HgbA1c categories were also associated with greater variability in HgbA1c levels during the analysis period. Similarly, greater glucose variability was associated with higher mean glucose levels by trend analysis. High, not low, HgbA1c levels are associated with greater risk of severe hypoglycemia, which may derive from glucose variability in the setting of treatment for hyperglycemia. High HgbA1c and glycemic variability are associated with increased risk of hypoglycemia in individuals with diabetes and ESRD.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".