Risk of exposure to blood products during pregnancy: guidance for Zika and other donor deferral policies
Bibliographic record
Abstract
BACKGROUND: Exposure to blood products during pregnancy carries a potential risk of transfusion transmission of infectious agents. Blood agencies have historically sought optimal deferral and testing strategies to protect blood supplies in part to ensure the protection of pregnant women and their unborn infants. The Zika virus outbreak of 2016 has heightened attention to these concerns. In the current context of the recent Zika outbreak, and also more broadly, data are needed to shed light on the likelihood of exposure to blood products throughout pregnancy to inform policy. STUDY DESIGN AND METHODS: Hospital administrative data from a large tertiary care center were examined for outpatient and nondelivery inpatient transfusion events in pregnant women between January 1, 2007, and December 31, 2013. Those pregnancies resulting in miscarriage were excluded from analysis. RESULTS: A total of 45,179 pregnancies resulting in delivery of a live or stillborn infant were documented at The Ottawa Hospital during the study period. Our findings indicate that 0.124%, or 1240 in 1,000,000, expectant mothers received transfusion at some point during their pregnancies with 0.04%, or 400 per 1,000,000, expectant mothers receiving a transfusion in the first trimester. CONCLUSION: Data from a large tertiary care center suggest that the risk of maternal and fetal exposure to blood products by transfusion during any stage of pregnancy is very low. Such a low likelihood of transfusion should be taken into consideration when developing donor deferral policies designed to protect women against transfusion-associated infection transmission during pregnancy and could inform evaluations of the risk of prenatal transfusion transmission of the Zika virus.
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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 | 0.014 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".