Association Between Glycemia and Mortality in Diabetic Individuals on Renal Replacement Therapy in the U.K.
Bibliographic record
Abstract
OBJECTIVE: In the U.K., one-third of patients receiving treatment with dialysis have diabetes. Guidelines from organizations representing patients with renal disease or diabetes advocate tight glycemic control in patients with end-stage renal disease, despite glucose-lowering trials having excluded these patients. RESEARCH DESIGN AND METHODS: Using national U.K. Renal Registry data, we tested whether glycemia as measured by hemoglobin (Hb) A(1c) (HbA(1c)) level is associated with death in adults with diabetes starting hemodialysis or peritoneal dialysis between 1997 and 2006, and observed for at least 6 months. Of 7,814 patients, we excluded those who had died within 6 months; had received transplants; were lost/recovered; or lacked measures of HbA1c, ethnicity, or Hb. Categorizing HbA1c measured in the first 6 months of starting dialysis as <6.5% (<48 mmol/mol), 6.5-7.4% (48-57 mmol/mol) (reference value), 7.5-8.4% (58-68 mmol/mol), and ≥8.5% (≥69 mmol/mol), we adjusted in proportional hazards models for age, sex, ethnicity, deprivation, year, dialysis type, and Hb, and tested for interactions. RESULTS: Of 3,157 patients observed for a median time of 2.7 years, 1,688 died. For patients ≥60 years of age, we found no association between HbA1c and death; among younger patients, relative to those with HbA(1c) values 6.5-7.4%, the hazard ratio for HbA(1c) level 7.5-8.4% was 1.2 (95% CI 0.9-1.5), and for HbA(1c) level >8.5% was 1.5 (1.2-1.9). The projected difference in median survival time between younger patients with a reference HbA1c value versus >8.5% was 1 year. CONCLUSIONS: In the absence of trials, and confounding notwithstanding, these observational data support improved glycemic control in younger patients prior to and during dialysis.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".