<scp>KEL1</scp> negative red cell transfusions for females of current or future <scp>child‐bearing</scp> potential: A clinical impact and feasibility study
Bibliographic record
Abstract
BACKGROUND: Anti-K is an alloantibody stimulated in response to the KEL1 antigen and may cause hemolytic disease of the fetus and newborn (HDFN). Provision of KEL1 negative blood to females of child-bearing potential was not our practice. We assessed the impact of our policy and assessed feasibility of a KEL1 negative transfusion policy. STUDY DESIGN AND METHODS: This is a cohort study spanning Jan 1, 2007-Jun 30, 2017 in Hamilton, Canada. Data were obtained via our institution's transfusion database. Chart reviews of females age ≤45 with anti-K were performed; data on RBC KEL1 phenotype were obtained from the blood supplier when needed to ascertain the cause of alloimmunization. Descriptive analysis of hospital KEL1 negative inventory demand and supply was performed. RESULTS: From Jan 2007-Jun 2017, 8.6% of all RBC units transfused were provided to females age ≤45. There were 111 females with detectable anti-K. Median age at time of antibody detection was 34 years (interquartile range 27-40) and 28 of 111 (25.2%) patients may have been alloimmunized by transfusion. Of 49 pregnancies, seven had complications due to anti-K. We estimated that our existing RBC inventory (with 16% units known to be KEL1 negative in 2017) is sufficient to meet demand and support a KEL1 negative transfusion policy for females age ≤45. CONCLUSION: Transfusion was responsible for alloimmunization in 25% of females with anti-K over 10 years. Analysis of supply and demand can be used to inform feasibility of a KEL1 negative transfusion policy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".