Prevalência de hemoglobinopatias na população adulta brasileira: Pesquisa Nacional de Saúde 2014-2015
Bibliographic record
Abstract
OBJECTIVE: To describe the prevalence of hemoglobinopathies in the Brazilian adult population, according to laboratory tests from the National Health Survey. METHODS: A descriptive study was carried out with National Health Survey laboratory data collected between 2014 and 2015. The hemoglobinopathies test was performed using the High Performance Liquid Chromatography method. The results of the individual tests were interpreted as providing normal, homozygous or heterozygous results for S, C and D hemoglobin, in addition to other possible hemoglobinopathies. Prevalence of hemoglobinopathies according to gender, skin color, region, age and schooling was estimated. RESULTS: Hemoglobinopathies were present in 3.7% of the population. The main ones were the sickle cell trait (2.49%), thalassemia minor (0.30%) and suspected thalassemia major (0.80%). In relation to the sickle cell trait and suspected thalassemia major, there was a statistically significant difference for the skin color variable (p<0.05). The prevalences found for sickle cell trait according to skin color was: 4.1% among dark-skinned blacks, 3.6% among light-skinned blacks, 1.2% among whites, and 1.7% among others. CONCLUSION: The most prevalent hemoglobinopathies were the sickle cell trait and minor thalassemia, and were predominate among light- and dark-skinned black people. The study helps in identifying hemoglobinopathies and in genetic counseling in pre-conception.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".