Rh disease in Mexico: evaluating regional and institutional differences in treatment availability and disease management.
Bibliographic record
Abstract
BACKGROUND: Rh disease occurs following maternal alloimmunization, which can develop due to RhD blood group antigen incompatibility between a mother and her fetus. Despite developing robust clinical protocols for effective immunoprophylaxis over the last 50+ years, a significant global burden of Rh disease still exists, particularly in low/middle-income countries such as Mexico. MATERIALS AND METHODS: This study examined disparities in the allocation of maternal and child health resources, as well as clinical knowledge regarding Rh disease, to gain insight into why Rh disease remains prevalent in Mexico. To this end, an 11-question survey was sent to members of the Federación Mexicana de Colegios de Obstetricia y Ginecología (FEMECOG) to evaluate their knowledge of the availability and implementation of anti-RhD immunoglobulin prophylaxis in their practices and institutions, and about managing Rh disease by monitoring fetal anemia risk and providing intrauterine treatment when necessary. Responses were separated by region, and chi-square two-by-two contingency tests were performed to evaluate regional and institutional differences. RESULTS: Significant variations in prevention and treatment were found within the Mexican healthcare system, particularly, with regard to providing anti-RhD immunoglobulin to prevent alloimmunization, which is critically important for preventing Rh disease. Specifically, Regions 5, 6, and 7 were most lacking in this regard. DISCUSSION: This study highlights differences in the Mexican healthcare system in preventing and treating Rh disease. Closing the gap in the availability of anti-RhD immunoglobulin should take priority in future efforts aimed at providing equitable care, because this will lead to the more preferable outcome of preventing Rh disease, rather than forcing patients to seek out more complex measures for treating Rh disease after it develops. These data can be used to create strategies to understand and eliminate these healthcare disparities.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".