Blood, sweat, and tears: Red Blood Cell‐Omics study objectives, design, and recruitment activities
Bibliographic record
Abstract
BACKGROUND: The Red Blood Cell (RBC)-Omics study was initiated to build a large data set containing behavioral, genetic, and biochemical characteristics of blood donors with linkage to outcomes of the patients transfused with their donated RBCs. STUDY DESIGN AND METHODS: The cohort was recruited from four US blood centers. Demographic and donation data were obtained from center records. A questionnaire to assess pica, restless leg syndrome, iron supplementation, hormone use, and menstrual and pregnancy history was completed at enrollment. Blood was obtained for a complete blood count, DNA, and ferritin testing. A leukocyte-reduced RBC sample was transferred to a custom storage bag for hemolysis testing at Storage Days 39 to 42. A subset was recalled to evaluate the kinetics and stability of hemolysis measures. RESULTS: A total of 13,403 racially/ethnically diverse (12% African American, 12% Asian, 8% Hispanic, 64% white, and 5% multiracial/other) donors of both sexes were enrolled and ranged from 18 to 90 years of age; 15% were high-intensity donors (nine or more donations in the prior 24 mo without low hemoglobin deferral). Data elements are available for 97% to 99% of the cohort. CONCLUSIONS: The cohort provides demographic, behavioral, biochemical, and genetic data for a broad range of blood donor studies related to iron metabolism, adverse consequences of iron deficiency, and differential hemolysis (including oxidative and osmotic stress perturbations) during RBC storage. Linkage to recipient outcomes may permit analysis of how donor characteristics affect transfusion efficacy. Repository DNA, plasma, and RBC samples should expand the usefulness of the current data set.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".