Addressing Social Determinants of Health in Linkage-to-Care Interventions for Hepatitis C: A systematic review v1
Bibliographic record
Abstract
As of 2017, there were more than three million individuals in the United States infected with Hepatitis C virus (HCV) [1]. Because most cases are asymptomatic, leading to a higher rate of unreported cases, this number is suspected to be much higher. Furthermore, there is a rise of HCV cases, and this increase documented since 2013 is primarily due to injection drug transmission alongside the rise of the opioid epidemic [2]. The Centers for Disease Control and Prevention (CDC) encourages HCV testing for all adults at least once in their lifetime, and those that test positive for HCV should be linked to treatment [3]. In 2013, only 13%-18% of patients with HCV received treatment in that year, indicating that there are barriers to treatment, such as lack of treatment acceptance, co-existing conditions, extensive treatment, side-effects, and access to treatment[4]. Thus, health disparities may ensue and the eradication of HCV as a public health issue becomes even more challenging. Edlin and Winkelstein (2014) suggested that to achieve optimal HCV eradication, social determinants of health, such as homelessness, need to be addressed [5]. Because there is room for improvement in linkage-to-care opportunities, it is important to explore whether linkage-to-care interventions address social determinants of health, and if so, which ones, to obtain a better understanding of what may help improve patient outcomes. References: U.S. Department of Health and Human Services. 2019.Retrieved June 11, 2020, fromhttps://www.hhs.gov/opa/reproductive-health/fact-sheets/sexually-transmitted-diseases/hepatitis-c/index.html Centersfor Disease Control and Prevention(CDC). 2019. Commentary portion of U.S. 2017 Surveillance Data for Viral Hepatitis. Retrieved June 11, 2020, fromhttps://www.cdc.gov/hepatitis/statistics/2017surveillance/index.htm Schillie, S., Wester, C., Osborne, M., Wesolowski, L., & Ryerson, A.B. (2020). CDC Recommendations for Hepatitis C Screening Among Adults – United States, 2020. MMWR Recommendations & Reports, 69(2), 1-17.https://www.cdc.gov/mmwr/volumes/69/rr/rr6902a1.htm Infectious Disease Society of America. 2019. HCV Testing and Linkage to Care: HCV Guidance. Retrieved June 23, 2020, fromhttps://www.hcvguidelines.org/evaluate/testing-and-linkage Edlin, B. & Winkelstein, E. (2014). “Can Hepatitis C be eradicated in the U.S.?”Antiviral Research.110: 79-93.https://dx.doi.org/10.1016/j.antiviral.2014.07.015
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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 | 0.010 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".