Comparison of Clinical and Sociodemographic Characteristics of Hemodialysis Modalities
Bibliographic record
Abstract
Background: Hemodiafiltration (HDF) is a well-established kidney replacement therapy. However, the selection of the dialysis modality is normally made according to individual characteristics. We aimed to compare clinical and sociodemographic characteristics (with special interest in mineral bone disease (MBD) markers) according to hemodialysis modality among patients treated in 4 countries in Latin America in the first version of the global database (Apollo Dial DB). Methods: Apollo Dial DB includes adult dialysis patient data from a global kidney network during Jan 2018-Mar 2021 (Fresenius Medical Care, Bad Homburg, DE). Data anonymization was performed in alignment with recommendations from a re-identification risk determination (Privacy Analytics, Ontario, CA). We included patients with HD or HDF for >90 days, with >90% of their assigned treatment for 12 weeks. Results: A total of 20,350 patients were analyzed, 17,916 (88%) patients on HD, 2,434 (12%) on HDF. Figure shows differences in sociodemographic and clinical characteristics of the patient groups. Patients in HDF group were younger, had higher vintage, lower prevalence of preexisting CVD and diabetes, higher prevalence of fistula as vascular access, larger use of Vitamin D analogs, phosphate binders and calcimimetics. OCM Kt/V and blood flow were higher among HDF compared to HD. Slightly higher target achievement were observed for calcium and phosphate parameters on HDF group. Conclusion: Prescription of HDF as kidney replacement therapy is not solely determined by specific guidelines but significantly influenced by various patients’ characteristics that may lead to selection bias when analyzing the treatment effect. Hyperphosphatemia is particularly an indication for HDF in most LatAm countries, therefore future research is needed to evaluate how HDF affects MBD markers independently of other patient’s factors. 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 | 0.001 | 0.003 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".