Seroprevalencia de virus Hepatitis B en gestantes al momento del parto
Bibliographic record
Abstract
Worldwide, there is an alert due to the increase in the seroprevalence of hepatitis B virus (HBV). This can cause up to 3.5% of chronic diseases, of which 40% present secondary complications and/ or early death. OBJECTIVE: To determine the seroprevalence of HBV in pregnant women at the time of delivery. PATIENTS AND METHOD: Observational, descriptive, cross-sectional study with cross-association between 2018 and 2019 at the Hospital Carlos Van Buren (HCVB), in Valparaiso, Chile. All pregnant women admitted for delivery care or with an immediate newborn who had HBV surface antigen study were included. Data were collected from the pregnant woman (age, nationality, education level, parity, type of delivery, and peripartum HIV-syphilis serology) and the newborn (gestational age, weight, and APGAR score). Inferential and multivariate analysis was performed using the Stata software. RESULTS: 1,355 pregnant women were analyzed. 87.7% were Chilean, 5.5% Haitian, 4.2% Venezuelan, and 2.6% were of other nationalities. 0.3% were positive for HBV. The prevalence of HBV in Chileans was 0.08% and in Haitians 4%. Haitian nationality was at higher risk of HBV (OR = 83) vs. Chilean nationality (p = 0.0001). None presented coinfection with HIV and/or syphilis. CONCLUSIONS: HBV seroprevalence in HCVB pregnant women was 0.3%, similar to that described in the general population in Chile. There was no coinfection with other sexually transmitted diseases. The only predictor of HBV infection was Haitian nationality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".