Variation in Hemoglobin Distribution in Patients With CKD: The International Network of CKD Cohorts (iNET-CKD)
Bibliographic record
Abstract
Background: Hemoglobin (Hb) has a narrow target range in patients with CKD but little is known about geographical differences in Hb distribution. The aims of this global collaborative study were to compare the distribution of Hb across international CKD populations and evaluate predictors of Hb. Methods: Two-stage individual participant data meta-analysis of cross-sectional data from participating iNET-CKD cohorts. Included participants were >18 years with nondialysis CKD (eGFR <60 mL/min). Using a standardized protocol with harmonized variable definitions, the relationships between candidate predictors and Hb were evaluated in linear regression models and estimates were subsequently pooled in a random effects model. Results: A total of 54,368 participants from 19 cohorts (median eGFR 17 to 49 mL/min) were included with broad geographic representation. Hb values varied substantially among the cohorts even within strata of eGFR and sex (Figure). Across the eGFR range, women had a lower Hb compared to men; however, these differences were smaller at lower eGFR (mean difference 13.2 g/L (95% CI 10.8-15.6) at eGFR of 60 mL/min versus 5.2 g/L (95% CI 3.5-7.0) at eGFR of 15 mL/min, p<0.001 for interaction). In a multivariable model, lower eGFR, female sex, older age, lower body mass index and diabetic kidney disease were independent predictors of a lower Hb value, but this only explained a minority of variance in Hb (median R2 of 21% [range 7 to 44%] across the cohorts).Figure:: Hemoglobin distribution among international CKD cohorts by eGFR category and sex.Conclusions: There are substantial regional differences in Hb distribution among individuals with CKD. The majority of variance in Hb is unexplained by demographics, GFR or comorbidities. A better understanding of international variation in Hb, and the factors that contribute to it, could change the way Hb values are interpreted in CKD. Funding: Commercial Support - GSK
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".