A Real-World Longitudinal Analysis of Anemia Treatment Prescriptions in Non-Dialysis-Dependent CKD Patients, a CKDopps Study
Bibliographic record
Abstract
Background: Previously lacking in the literature, this analysis aims to comprehensively describe longitudinal patterns of anemia management, including prescriptions of ESA and iron replacement, for non-dialysis dependent chronic kidney disease (NDD-CKD) stage 3 to 5 patients under nephrologist care. Methods: We analyzed data from a prospective cohort of 2455 NDD-CKD patients from Brazil, Germany and the US, who were not using anemia medications (oral iron, intravenous [IV] iron, or erythropoiesis stimulating agent [ESA]) at enrollment in the Chronic Kidney Disease Outcomes and Practice Patterns Study (CKDOPPS). We reported the cumulative incidence function (CIF) [HK1] of anemia treatment initiation, stratified by patient characteristics. For patients that started therapy, we report the frequency of medication type at the moment of initiation, as well as switches and discontinuation over 12 months. Results: The CIF of any anemia treatment initiation at 12 months was 54% for patients with Hb <10 g/dL. For oral iron therapy, the CIF at 12 months was 26% (19%, 32%) for TSAT<20%, and 22% (17%, 28%) for ferritin <100. For IV iron use, CIF at 12 months was 6% (3%, 11%) for patients with TSAT<20% and 4% (2%, 7%) for patients with ferritin <100ng/mL. For ESA use, the CIF at 12 months was 38% (29%, 47%) for patients with Hb <10 g/dL, and 11% (8%, 14%) for Hb 10 to <12 g/dL. Medication types at initiation and longitudinal treatment patterns (switches and discontinuation) are shown in the figure. Conclusions: In a period of 12 months, anemia medication is initiated in a limited number of NDD-CKD patients with clinical signs that would indicate to do so. This longitudinal analysis using data from the real-world setting, call attention to a sub-optimally management of anemia in the NDD CKD setting.Anemia medication starts and switches within a year of follow-up.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".