Anemia in Pregnancy: A Pragmatic Approach
Bibliographic record
Abstract
Importance Anemia is common in pregnancy, ranging from 5.4% in developed countries to more than 80% in developing countries. Anemia in pregnancy has been associated with prematurity, low birth weight, and adverse pregnancy outcomes. Objective This review uses clinical vignettes to illustrate the clinical presentations, approach to diagnosis, maternal and fetal implications, and treatment for the common etiologies of anemia in pregnancy. Evidence Acquisition Literature review. Results Normal physiological changes in pregnancy result in alterations of hematological parameters particularly in a reduction of hemoglobin (Hb) concentration. Consequently, the Hb used to define anemia in pregnancy is lower than in nonpregnant patients. As there is an increased requirement of iron in pregnancy, it is not unexpected that iron deficiency remains the most common cause of anemia and warrants a preemptive approach to prevent a further reduction in Hb. The syndromes associated with microangiopathic hemolytic anemia may pose a diagnostic challenge, as there are several potential etiologies that may be difficult to differentiate, and microangiopathic hemolytic anemia can be associated with significant maternal and fetal morbidity and mortality. Anemia secondary to sickle cell disease and autoimmune hemolytic anemia merit special attention because there are risks secondary to red blood cell transfusion and risks to withholding transfusion. Conclusions and Relevance Anemia in pregnancy is potentially associated with maternal and fetal adverse outcomes. Providing evidence-based care is essential to achieving the best pregnancy outcomes. Target Audience Obstetricians and gynecologists, family physicians. Learning Objectives After completing this activity, the learner should be better able to describe the normal physiological changes in hematological parameters in pregnancy, recognize common and potentially life-threatening diseases manifested as anemia, and develop an approach to anemia in pregnancy.
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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 | 0.018 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".