Applying molecular immunohematology discoveries to standards of practice in blood banks: now is the time
Why this work is in the frame
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Bibliographic record
Abstract
Lessons from more than 100 years of immunohematology exemplify that many critical discoveries were made serendipitously and their more rapid implementation could have benefited transfusion recipients and pregnancies. Constituents of blood that are not essential for the attempted therapeutic benefit of a transfusion are largely removed from today's blood products. We are now moving on to avoid unnecessary exposure to potentially harmful constituents of the therapeutically required cells, like blood group antigens that are foreign to the patient. Cost efficacy needs to be kept in mind but may eventually prove much better than anticipated, once hidden benefits are captured, as we show by examples from past immunohematologic developments. Here, we detail clinical applications for molecular immunohematology advances including "dry-matching" that will improve transfusion outcomes and argue for their widespread implementation by rapid timelines through standards of practice.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it