The prevalence of anti‐K in Canadian prenatal patients
Bibliographic record
Abstract
BACKGROUND: Anti-KEL1(K) is a major cause of hemolytic disease of the fetus and newborn. We utilized data from prenatal testing of patients in Western Canada to determine the frequency of anti-K. In Manitoba, we evaluated the frequency of transfusion as the likely cause for alloimmunization. We reviewed international practices to prevent alloimmunization. STUDY DESIGN AND METHODS: Prenatal patients undergo antibody screening using an automated testing platform and uniform testing algorithm. Data on the frequency of antibodies, transfusion history, and donor K typing were extracted from the relevant databases at Canadian Blood Services. National standards were reviewed with the help of local experts. RESULTS: Anti-K was found in 397 of 390,193 patients from 2011 to 2013 (1.02 per 1000) and was the second most frequent antibody after anti-E. In Manitoba, 26 of 75 (35%) anti-K patients had received transfusions in the province since 2001; 14 of the 26 (54%) had received at least one K+ RBC unit and three had received all K- units, while in nine, donor K typing was incomplete. Only eight of the 26 had previous pregnancies, three with K+ partners. International practice varies; however, prophylactic use of matched or K- units is standard in many European countries. CONCLUSIONS: Anti-K was found in 0.1% of prenatal patients. Although our data on the history of transfusion are incomplete, they demonstrate that transfusion with a K+ unit is a major cause of alloimmunization. Given advances in phenotyping and genotyping technologies, prophylactic matching should be considered in Canada.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".