Kidney Failure Etiology and Modality Worldwide: Apollo Dial DB
Bibliographic record
Abstract
Background: We used a global database representing kidney care across six continents, Apollo Dial DB, to evaluate modality use by kidney failure etiology. Methods: Apollo Dial DB captures 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). The data has >360 variables (demographics, dialysis, diagnoses, labs, medications, surveys, & outcomes) from 01Jan2018-31Mar2021. Kidney failure etiology considered diabetes, other, or unspecified causes. Modality rates after primary dialysis initiation were assessed by etiology group overall, and by world region. Modality use considered hemodialysis (HD) only, peritoneal dialysis (PD) ever, or transplant. Results: Of 543,169 adults with kidney failure worldwide, 455,769 (80.6%) had a known etiology (diabetes=39.8%, other causes=40.8%, unspecified=19.4%). Overall, 84.8% used HD, 11.0% used PD, and 4.1% used a transplant after dialysis initiation. Modality use by etiology was consistent worldwide for HD and PD, yet kidney failure due to diabetes was associated with lower transplant rates (Figure 1). Regional differences were observed, with lower PD and transplant rates for kidney failure due to diabetes in some regions. Further, large regional differences in unspecified kidney failure etiology were present. Conclusion: Kidney failure etiology appears to influence modality use. About 40% of people with kidney failure due to diabetes use HD and PD, yet transplant rates are >50% lower. Regional variations exist and kidney failure due to diabetes is also associated with lower PD rates in select regions. Unspecified kidney failure etiology varied by region and further analyses are needed. Funding: Commercial Support - Fresenius Medical Care
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".