Regional Differences in Kidney Replacement Therapy in Diabetic Kidney Disease: Apollo Dial DB
Bibliographic record
Abstract
Background: It is unknown if kidney replacement therapy (KRT) types vary in diabetic kidney disease (DKD). We assessed KRT patterns by primary kidney failure etiology in a global database (Apollo Dial DB) representing care in six continents. Methods: Apollo Dial DB contains longitudinal observation-level data (each treatment, lab, value) from 40 countries in a kidney care network (Fresenius Medical Care, Bad Homburg, DE). Data anonymization was performed in alignment with recommendations from a re-identification risk determination (Privacy Analytics, Ontario, CA). Dataset has >360 variables (demographics, dialysis, diagnoses, labs, medications, surveys, & outcomes) from 01Jan2018-31Mar2021. Analysis assessed KRT rates after dialysis initiation stratified by region. Modality was defined as use of hemodialysis (HD) only, peritoneal dialysis (PD) ever, or transplant. Results: Among 543,169 adults (age ≥18 years) in 40 countries, 455,769 had a known kidney failure etiology (diabetes=39.8%, other=40.8%, unspecified=19.4%). Overall, 84.8% used HD only, 11.0% used PD, and 4.1% received a transplant for KRT after dialysis initiation. KRT used in DKD showed 40.7% used HD only, 39.1% used PD, and 21.9% received a transplant. PD and transplant rates differed across regions, with lower PD and transplant rates in DKD (Figure 1). Regional differences in unspecified etiology were also present. Conclusion: KRT patterns in DKD may differ from other etiologies. Worldwide, transplant occurred half as often in DKD; greater regional variations existed. Regionally, PD was less frequently used in DKD, specifically in Northern and Latin America, South Africa, and Eastern Europe. Kidney failure etiology is largely unspecified in select regions. Opportunities may exist in expanding KRT options in DKD. Funding: Commercial Support - Fresenius Medical Care
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".