Molecular immunohaematology round table discussions at the AABB Annual Meeting, Philadelphia 2014.
Bibliographic record
Abstract
Use of molecular-based immunohaematology testing is becoming more widespread worldwide in laboratories that are accustomed to the use of blood group serology alone. Molecular immunohaematology issues may be challenging even for some established professionals in the field of blood group serology. At an international meeting, we offered round table discussions on four patient-related and two donor-related topics, which are current and possibly controversial. Six molecular immunohaematology questions were addressed: applications for highly contagious infections, such as Ebola; utility after transfusions in the preceding three months; root cause analysis for unexplained occurrence of anti-D; acceptable turnaround time for red cell genotyping of patients; criteria for donor cohorts to be genotyped; and quality assurance for discrepancies between serological phenotype and licensed red cell genotyping. The opinions polled in this workshop with an international assemblage of more than 100 transfusion medicine specialists were discussed in the light of education and training opportunities and the development of guidance in the field. We provide a summary report of the participants’ input to our questions and discuss the topics.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.320 | 0.105 |
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".