Bibliographic record
Abstract
A stressful predicament for blood bankers!Monoclonal immunotherapeutic drugs are becoming an effective way to attack certain cancers and other conditions.Some of these antibodies recognise red blood cells (RBCs) as well as their intended cancer cell targets and can create considerable havoc in transfusion services when trying to provide compatible blood for transfusion to those patients who are receiving these medications. [1][2]2][3] Anti-CD38 (daratumumab; DARA) is one example that has been used for a few years now and is FDA approved for use in multiple myeloma patients; unfortunately, it causes panagglutination in serologic tests. 1,4,5The good news is that these serologic reactions are relatively weak (1+ to 2+), and various ways have been reported around the anti-CD38 panagglutination, enabling elucidation of potentially clinically significant underlying alloantibodies. 4These various approaches include the use of dithiothreitol (DTT) and other means to work around this issue and allow more assurances that no potentially clinically significant alloantibodies underly the panagglutination. 4,5fortunately, another monoclonal antibody, termed Hu5F9-G4 (anti-CD47), is an antibody that targets CD47 on cells.CD47 is found in high density on many types of cancer cells and is especially highly expressed on RBCs.CD47 acts, presumably, as a signal to monocytemacrophages to not phagocytose the cells 3,6 , and anti-CD47 can block the "don't eat me" signal and allow cells to be phagocytosed.When anti-CD47 is administered to certain cancer patients, it is given at very high dosage, which creates huge problems for transfusion services during their serologic evaluations related to type and screen and provision of compatible blood.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".