Applying molecular immunohematology discoveries to standards of practice in blood banks: now is the time
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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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 | 0.071 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.018 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".