Naturally nonanemic dialysis patients: Who are they?
Bibliographic record
Abstract
Introduction Not only anemia, but also erythropoiesis stimulating agent (ESA)s for treating anemia may adversely affect prognosis of chronic hemodialysis patients. Various features of naturally (with no ESA usage) nonanemic patients may be useful for defining several factors in the pathogenesis of anemia. Methods Data, retrieved from the European Clinical Database (EuCliD)-Turkey on naturally nonanemic prevalent chronic hemodialysis patients (n: 201) were compared with their anemic (those who required ESA treatment) counterparts (n: 3948). Findings Mean hemoglobin values were 13.5 ± 0.8 and 11.5 ± 0.9 g/dL in nonanemic and anemic patients, respectively (P < 0.001). Nonanemia status was associated with younger age, male gender, longer dialysis vintage, nondiabetic status, more frequent hepatitis-C virus seropositivity and more frequent arteriovenous fistula usage. Serum ferritin and CRP levels and urea reduction ratio were higher in ESA-requiring patients. One (99%) and two (95.3%) years survival rates of the "naturally nonanemic" patients were superior as compared to anemics (91.0% and 82.6%, respectively), (P < 0.001). Discussion "Naturally nonanemic" status is associated with better survival in prevalent chronic hemodialysis patients; underlying mechanisms in this favorable outcome should be investigated by randomized controlled trials including large number of patients.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".