Unique hemoglobin A1c level distribution and its relationship with mortality in diabetic hemodialysis patients
Bibliographic record
Abstract
Diabetic hemodialysis patients with hemoglobin A1c (HbA1c) levels below 6.5% and over 8.0% face a higher mortality risk. To determine the optimal glycemic control in Japanese patients, we examined the association between HbA1c and mortality in 2,300 Japanese diabetic patients on maintenance hemodialysis with HbA1c levels determined at enrollment in the Japanese Dialysis Outcomes and Practice Patterns Study (JDOPPS) phases 2-5, using Cox regression analysis with adjustment for baseline age, sex, dialysis vintage, 12 general comorbidities, hemoglobin, albumin and creatinine levels, and insulin use; stratification by JDOPPS phase; and facility clustering taken into account. Overall, 54% of patients had HbA1c levels under 6.0, including 14% with HbA1c levels under 5.0. Insulin or oral diabetes medications were used less frequently in patients with higher HbA1c levels. The dependence of mortality on HbA1c level was U shaped. When the group with the lowest mortality (HbA1c 6.0-7.0) was used as a reference, the hazard ratios for HbA1c categories under 5.0, 5.0-6.0, 7.0 to under 8.0, and 8.0 and greater were, respectively, 1.56 (95% confidence interval, 1.05-2.33), 1.26 (0.92-1.71), 1.23 (0.79-1.89), and 2.10 (1.32-3.33) in the adjusted model. The HbA1c level was not associated with self-reported hypoglycemic episodes in JDOPPS phase 5. The HbA1c levels in diabetic hemodialysis patients differ considerably between Japan and those reported from Western countries. Thus, our findings highlight the importance of domestic guidelines for glycemic control by race and country.
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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 | 0.001 | 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) | 0.002 | 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".