The <scp>G</scp>lycemic <scp>I</scp>ndices in <scp>D</scp>ialysis <scp>E</scp>valuation (<scp>GIDE</scp>) study: Comparative measures of glycemic control in diabetic dialysis patients
Bibliographic record
Abstract
The validity of hemoglobin A1c (HgbA1c) is undergoing increasing scrutiny in the advanced CKD/ESRD (chronic kidney disease/end-stage renal disease) population, where it appears to be discordant from other glycemic indices. In the Glycemic Indices in Dialysis Evaluation (GIDE) Study, we sought to assess correlation of HgbA1c with casual glucose, glycated albumin, and serum fructosamine in a large group of diabetic patients on dialysis. From 26 dialysis facilities in the United States, 1758 diabetic patients (hemodialysis = 1476, peritoneal dialysis = 282) were enrolled in the first quarter of 2013. The distributions of HgbA1c and the other glycemic indices were analyzed. Intra-patient coefficients of variation and correlations among the four glycemic indices were determined. Patients with low HgbA1c values were both on higher erythropoietin (ESA) doses and more anemic. Serum glucose exhibited the highest intra-patient variability over a 3-month period; variability was modest among the other glycemic indices, and least with HgbA1c. Statistical analyses inclusive of all glycemic markers indicated modest to strong correlations. HgbA1c was more likely to be in the target range than glycated albumin or serum fructosamine, suggesting factors which may or may not be directly related to glycemic control, including anemia, ESA management, and iron administration, in interpreting HgbA1c values. These initial results from the GIDE Study clarify laboratory correlations among glycemic indices and add to concerns about reliance on HgbA1c in patients with diabetes and advanced kidney disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".