<scp>TSAT</scp> is a better predictor than ferritin of hemoglobin response to Epoetin alfa in <scp>US</scp> dialysis patients
Bibliographic record
Abstract
Clinical guidelines recommend concurrent treatment of anemia in end-stage renal disease with erythropoiesis-stimulating agents (ESAs) and iron. However, there are mixed data about optimal iron supplementation. To help address this gap, the relationship between iron markers and hemoglobin (Hb) response to ESA (Epoetin alfa) dose was examined. Electronic medical records of 1902 US chronic hemodialysis patients were analyzed over a 12-month period between June 2009 and June 2010. The analysis included patients who had at least one Hb value during each 4-week interval for four consecutive intervals (k - 2, k - 1, k, and k + 1; k is the index interval), received at least one ESA dose during intervals k - 1 or k, had at least one transferrin saturation (TSAT) value at interval k, and at least one ferritin value during intervals k - 2, k - 1, or k. Effect modification by TSAT and ferritin on Hb response was evaluated using the generalized estimating equations approach. Patients had a mean (standard deviation) age of 62 (15) years; 41% were Caucasian, 34% African American, 65% had hypertension, and 39% diabetes. Transferrin saturation, but not ferritin, had a statistically significant (P < 0.05) modifying effect on Hb response. Maximum Hb response was achieved when TSAT was 34%, with minimal incremental effect beyond these levels. Of the two standard clinical iron markers, TSAT should be used as the primary marker of the modifying effect of iron on Hb response to ESA. Long-term safety of iron use to improve Hb response to ESA warrants further study.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".