Integrated analysis of blood donor metabolic phenotypes and genetic traits on red blood cell transfusion effectiveness
Bibliographic record
Abstract
Recent large-scale population studies in humans and in murine models of red blood cell (RBC) function identified associations between metabolic phenotypes, or genetic traits linked to them, and transfusion effectiveness. These metabolic phenotypes were identified in independent studies focusing on different mechanistic aspects of the storage lesion. The lack of an integrated analysis raised the question as to whether these signatures were redundant measures of the same underlying processes or could be evaluated together to inform a Precision Medicine approach to clinical transfusion practice. To bridge this gap, we performed an integrated analysis in 5,386 patients who received 6,220 single-unit RBC transfusions, evaluating donor metabolic and genetic results from several studies on hemoglobin increments following RBC transfusion. Our results indicate that previously reported metabolic and genetic predictors of hemoglobin increments remain significant, with an effect size between 0.05 and 0.15 g/dL, when evaluated concurrently. Our observational findings indicate that transfusing RBC units from donors with specific genetic traits, are not only negatively associated with immediate effectiveness but also increased downstream RBC transfusion events, further highlighting the need for refined donor screening practices. Altogether, this evidence supports adoption of a Precision Medicine approach to transfusion practice, where genetic screening of donors at first donation and longitudinal metabolic profiling could inform blood inventory management and allocation strategies, ensuring optimal outcomes for transfusion recipients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 |
| 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 teacher head, 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".