A 13-Question Approach to Resolving Serological Discrepancies in the Transfusion Medicine Laboratory
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Laboratory professionals, consultants, and treating physicians may encounter discrepancies in serological testing results for numerous reasons; identifying the reason(s) for the presence of an unexpected antibody or antigen can be challenging. A question-based approach can be useful in identifying the underlying cause of the discrepancy. We describe a new approach to serological problems in a transfusion-service laboratory. The approach we outline herein is targeted towards a general transfusion medicine service, rather than a center that offers complex antibody investigations using specialized techniques. This question-based problem-solving approach considers patient factors including diagnosis, transfusion history, previous pregnancies, and medication history, along with serological test results: ABO and Rh groups, direct and indirect antiglobulin tests, reacting temperature of the antibody, effect of enzyme treatment of cells, strength of reactivity, and antibody reactivity with umbilical cord cells. We also demonstrate the usefulness of this approach through a case scenario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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