Bibliographic record
Abstract
In the United States, trauma occurs in 6% to 7% of pregnancies. Its severity may range from critical injuries where the mother’s life is at risk, to apparently minor injuries, which might not be associated with any worrisome symptoms. One of the risks associated with a traumatic event is fetomaternal hemorrhage—the transfer of fetal blood cells into the maternal circulation. If the maternal blood type is rhesus negative and the fetus is rhesus positive, even a small amount of blood can cause the mother to develop antibodies against the fetal Rho D antigen, thus becoming sensitized. In subsequent pregnancies, this can lead to hemolytic disease of the fetus or newborn, which, if severe, is associated with total body edema, hepatosplenomegaly and heart failure, and intrauterine death. Although there are no published studies specific to the US population, poor awareness of the risk of sensitization following trauma and underutilization of anti-RhD in the emergency department has been reported in countries such as Canada and the Untied Kingdom. This article reminds caregivers of the risk of rhesus sensitization following blunt trauma, in order that they can administer anti-RhD appropriately and hemolytic disease of the fetus or newborn can be prevented. Target Audience: Obstetricians & Gynecologists, Family Physicians Learning Objectives: After completion of this article, the reader should be able to state that blunt trauma to the abdomen during pregnancy has the potential of sensitizing the Rh-negative mother, recall that only a very small amount of fetal blood is required, and explain that treatment with anti-RhD is highly efficacious but underutilized.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".