Survey of newborn direct antiglobulin testing practice in <scp>United States</scp> and <scp>Canadian</scp> transfusion services
Bibliographic record
Abstract
BACKGROUND: We hypothesized that variability in practice exists for newborn immunohematology testing due to lack of consensus guidelines. We report the results of a survey assessing that variability at hospitals in the United States and Canada. STUDY DESIGN AND METHODS: An AABB Pediatric Subsection working party developed and validated a survey of newborn immunohematology testing practice. The survey was sent electronically to transfusion service leadership at teaching institutions. RESULTS: The response rate was 67% (61/91); 56 surveys meeting inclusion criteria were analyzed. Approximately 90% (50/56) were from birth hospitals and 16.1% (9/56) were from pediatric hospitals. Newborn immunohematology testing is ordered as a panel by 66.0% (33/50) of birth hospitals. ABO group and DAT is mandated before discharge in 14/56 (25.0%) and 13/56 (23.2%), respectively. About 76.8% (43/56) selectively perform a DAT according to blood blank or clinical parameters. The most common DAT practices include anti-IgG only testing by 73.2% (41/56) and use of umbilical cord specimen type by 67.9% (38/56). A positive DAT is a critical value for 26.8% (15/56) and followed with eluate testing when a maternal antibody screen is positive for 48.2% (27/56). In the setting of a non-ABO maternal red cell antibody, 55.4% (31/56), phenotype neonatal red cells when the DAT is positive. Group O RBC are transfused irrespective of the DAT result for 82.1%, (46/56). CONCLUSION: There is variability in newborn immunohematology testing and transfusion practice and potential overutilization of the DAT. Evidence-based consensus guidelines should be developed to standardize practice and to improve safety.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".