Hepatitis C and pregnancy outcomes: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: Many women living with hepatitis C (HCV) are of childbearing age. While the risk of vertical HCV transmission has been well established, the impact of HCV on pregnancy outcomes are equivocal, with some studies reporting risks of preterm birth, low gestational weight, gestational diabetes and hypertension, while other studies report no such risks. With the shift of the HCV treatment landscape to more effective, tolerable and shorter medications, understanding pregnancy outcomes of women living with HCV are an important consideration in order to provide a baseline from which to consider the usefulness and safety of HCV treatment for this population. The objective of this systematic review will be to investigate pregnancy outcomes associated with maternal HCV infection. METHODS AND ANALYSIS: This systematic review will incorporate articles relevant to pregnancy outcomes among women living with HCV (eg, gestational diabetes and caesarean delivery). Articles will be retrieved from academic databases including MEDLINE, EMBASE, CINAHL, clinicaltrial.gov and the Cochrane Library and hand searching of conference proceedings and reference lists. A database search will not be restricted by date, and conference abstract will be restricted to the past 2 years. The Newcastle-Ottawa Quality Assessment Scale will be used to assess the quality of the retrieved studies. Data will be extracted and scored independently by two authors. A narrative account will synthesise the findings to answer the objectives of this review. ETHICS AND DISSEMINATION: This systematic review will synthesise the literature on the pregnancy outcomes of women living with HCV. Results from this review will be disseminated to clinical audiences, community groups and policy-makers, and may support clinicians and decision-makers in developing guidelines to promote best outcomes for this population.
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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 | 0.077 | 0.073 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.022 | 0.015 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.100 | 0.013 |
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".