Data related to treatment optimisation for hepatitis C in the era of combination direct-acting antiviral therapy: systematic review and meta-analysis
Bibliographic record
Abstract
This data archive contains the following 7 files relating to treatment optimisation for hepatitis C in the era of combination direct-acting antiviral therapy: - Supplementary figure 1: Thematic map exploring strategies for treatment optimisation. The duration, combination and/or dose of a treatment regimen is optimised for the individual receiving therapy. Abbreviations: RBV - ribavirin; DAA - direct-acting antiviral; IFN - interferon. - Supplementary figure 2: Cochrane risk of bias tool for randomised controlled trials summary - review authors' judgements about each risk of bias item for each included study. Prepared using Cochrane Review Manager v5.3 (RevMan, RRID:SCR_003581). - Supplementary figure 3: Risk of bias graph - review authors' judgements about each risk of bias item presented as percentages across all included studies. Prepared using Cochrane Review Manager v5.3 (RevMan, RRID:SCR_003581). - Prisma 2009 Checklist: Completed checklist providing page numbers in the associated manuscript for the location of each element of the Prisma 2009 checklist. - HCV Treatment Optimisation Stata .do file: This is the .do file used to process and analyse the data. The proprietary statistical software 'Stata' was used to create this file. - HCV Treatment Optimisation Meta-analysis Data Sheet: This is the data extracted from trials included in this meta-analysis. The file was imported into Stata for analysis using the included .do file.- Supplementary Tables 1-6.docx:This single Word document contains the following 6 tables: - Supplementary table 1: Summary of Ovid search strategy - Medline and Embase. Last conducted on 4th July 2019. - Supplementary table 2: Individual study characteristics. The factors used for stratification or personalisation, treatment strategy adopted, and resultant SVR rates (intention-to-treat and per protocol) are presented for each treatment arm. - Supplementary table 3: Meta-regression - ‘maintain SVR group’. Clinical and methodological variables were subject to univariable random effects meta-regression. Only those variables with p≤0·1 on univariable analysis were carried forward to the multivariable model. Significance of variables in multivariable model taken at p≤0·05 level. Upper (UCI) and lower (LCI) 95% confidence intervals are presented. - Supplementary table 4: Meta-regression - improve SVR group. Clinical and methodological variables were subject to univariable random effects meta-regression. There were no significant associations and therefore a multivariable model was not constructed. Upper (UCI) and lower (LCI) 95% confidence intervals are presented. - Supplementary table 5: Modified Newcastle Ottawa Scale for quality assessment of nonrandomised studies. In this modified scale, a study can be awarded a maximum of one star for each item within the Selection and Outcome categories. The comparability domain was removed to account for the non-comparative nature of included studies (unmodified version can be found at: http://www.ohri.ca/programs/clinical_epidemiology/nosgen.pdf). - Supplementary table 6: Ongoing randomised controlled trials that are evaluating stratified or personalised treatment strategies. The associated study is a systematic review and meta-analysis which explores the impact of treatment optimisation strategies, such as stratified medicine or personalised medicine, in our current era of direct-acting antiviral therapy, applied to the treatment of hepatitis C virus.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.021 | 0.193 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.022 | 0.035 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.242 | 0.016 |
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".