Immunoglobulin M anti‐A and anti‐B titers in South Texas group O D+ male donors
Bibliographic record
Abstract
BACKGROUND: The success of whole blood (WB) in damage control resuscitation on the battlefield has generated interest in its use for civilian trauma. Blood centers must maintain a committed donor pool with low isoagglutinin titers to provide this product. Information regarding isoagglutinin titers in different donor populations will help with targeted recruitment of these donors. STUDY DESIGN AND METHODS: Sequential O D+ male donors with a history of two or more donations at a fixed site were tested for immunoglobulin (Ig)M anti-A and anti-B using a single titer cutoff of 256. Donors testing negative at this cutoff were considered "low titer" while positive donors were considered "high titer." Age and self-identified race/ethnicity were retrospectively obtained from the blood establishment computer system. Fisher's exact analysis was used for statistical analysis with a p value of less than 0.05 considered significant. RESULTS: Of 3274 donors, 426 tested as high titer, while 2848 tested as low titer. The data show an association of donor age and prevalence of high titers with older age groups showing a lower prevalence of high titers. In addition, different races/ethnicities have different prevalences of high titers with the Caucasian/white group showing a lower prevalence of high-titer donors versus the Hispanic and undeclared race groups. CONCLUSION: The prevalence of high-titer IgM anti-A or -B donors varies by age group and race/ethnicity in our data set. This information will provide information on what donor groups to target for collection of low-titer O WB.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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; both teacher heads agree on what is shown here.
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".