A prevalence of hepatitis C in pregnant women of Pakistan: A systematic review
Bibliographic record
Abstract
Hepatitis C is a major public health problem affecting 3% of the population around the world. However, worldwide prevalence of hepatitis C in pregnant women is variable. In Pakistan alone prevalence of hepatitis C in pregnant women varies from 0.7% to 36%. Unfortunately, a rising prevalence of HCV in pregnant women is making them vulnerable to complications. Therefore, the purpose of this analysis is to estimate a prevalence rate that represents the actual burden of hepatitis C in pregnant women; by pooling the prevalence of hepatitis C in pregnant women reported in 17 studies from Pakistan. Pakmedinet, PubMed through the National Center for Biotechnology Information (NCBI), MEDLINE and Google Scholar search engines were employed for literature search. Studies published in the English language from the year 2001 and onwards have been included in this review. Since there is a paucity of studies related to this topic, we did not exclude any published literature fulfilling inclusion criteria. The quality of each study was assessed using the Newcastle–Ottawa Scale. Paper review was done by two reviewers with data extracted and compiled in MS Excel. The registration number of the study is CRD42016042341. A total of 17 studies from Pakistan reported prevalence of hepatitis C in pregnant women; 3 each were conducted in Islamabad and Khyber Pakhtunkhwa respectively, 5 in Punjab and 6 studied were conducted in Sindh province. From 17 studies, we pooled 124635 pregnant women screened for hepatitis C and of them, 8342 women were found to be positive for Anti-HCV. The overall prevalence of hepatitis in pregnant women of Pakistan was 6.7% (95% CI: 6.5-6.8) while its range varied from 0.7% to 36%. In Pakistan, the overall prevalence of hepatitis C in pregnant women was 6.7%. Nonetheless these findings cannot be disregarded and should be used by Policymakers for considering the antenatal screening of hepatitis C in pregnant women.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".