Incidence of HIV and HCV in Injecting Drug Users: Systematic and Meta-analysis Review protocol
Bibliographic record
Abstract
Abstract introduction: HIV and HCV is a health concern in the word. Therefore, this systematic review and meta-analysis protocol study is aimed to estimate the incidence of HIV and HCV among People who inject drugs (PWID) by applying mathematical modeling. The primary purpose of this systematic review is to identify and review existing studies of HIV and HCV incidence in people who inject drugs, that use mathematical modeling. Methods and analysis: Studies including cohort, cross-sectional, case–control studies which were conducted to estimate incidence of HIV and HCV based on mathematical or evaluated effectiveness of mathematical models will be considered to enroll the review; a comprehensive search with Cochrane approach would be applied to identify relevant studies in electronic databases in the period of 2000 to 2019. This protocol was prepared according to the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) and we will search PubMed, EMBASE, Opengrey, WOS, SCOPUS, and Cochrane Library with no restriction of language. Study selection and data extraction will be performed by two independent reviewers. Assessment of risk of bias will be implemented using three quality test tools including Newcastle Ottawa Scale and ROBIS scale for cross-sectional studies and quantitative studies quality test tool for observational and interventional studies for other study Publication bias will be assessed by funnel plots, Begg’s and Egger’s tests. Heterogeneity will be evaluated using the I2 statistic and the χ2 test. In addition, subgroup analyses will be conducted for population and the secondary outcomes.
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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 | 0.048 | 0.079 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.023 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.101 | 0.009 |
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".