The Benefit of a Glucose-Sparing PD Therapy on Glycemic Control Measured by Serum Fructosamine in Diabetic Patients in a Randomized, Controlled Trial (IMPENDIA)
Bibliographic record
Abstract
BACKGROUND/AIMS: Poor glycemic control can lead to increased morbidity and mortality in peritoneal dialysis (PD) patients. Serum fructosamine may be a more reliable marker of glycemic control than HbA1c in dialysis patients. METHODS: We evaluated the effects of a glucose-sparing PD regimen on serum fructosamine. In the multicenter, controlled IMPENDIA trial, eligible diabetic PD patients were randomized (1:1) to a 24-hour combination of a glucose sparing regimen (n = 89) or a glucose-based therapy (n = 91). Serum fructosamine and HbA1c were measured at baseline, 3 months and 6 months; fructosamine measurements were corrected for serum albumin (AlbF). RESULTS: Serum fructosamine decreased from 297 to 253 µmol/l in the glucose-sparing group (95% confidence interval [CI] for the difference, -26 to -68, p < 0.001), and increased from 311 to 314 µmol/l in the glucose-only group (95% CI for the difference, -23 to +19, p = 0.87). The mean difference in change of fructosamine levels between groups at 6 months was 64 µmol/l (95% CI 29-99, p < 0.001). HbA1c decreased versus baseline in both groups (treatment difference 0.3%, p = 0.07). The correlation between AlbF and baseline fasting serum glucose was stronger than that seen between HbA1c and baseline fasting serum glucose (r = 0.47, p < 0.0001 and r = 0.31, p < 0.0001, respectively). CONCLUSION: A glucose-sparing regimen (P-E-N) improved glycemic control as measured by serum fructosamine. Further studies are needed to establish fructosamine targets that will reduce the morbidity risk related to hyperglycemia in PD patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".