Incidence and predictors of acquired resistance to second-line antituberculosis drugs during the course of multi-drug resistant tuberculosis treatment: protocol for a systematic review and meta-analysis
Bibliographic record
Abstract
Introduction To date, acquired resistance to second-line antituberculosis drugs (SLDs) during multi-drug resistant tuberculosis (MDR-TB) treatment is becoming a public health concern. Different studies have assessed the incidence of acquired resistance to SLDs. However, the findings are inconsistent and there is limited global evidence. Thus, we are going to assess the incidence and predictors of acquired resistance to SLDs during MDR-TB treatment. Methods and analysis We designed this protocol following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist. Electronic databases and grey literature sources will be searched systematically for articles published up to 25 March 2023. Studies reporting the incidence and predictors of acquired resistance to SLDs in MDR-TB patients will be explored. The studies will be managed using Endnote X8 citation manager and a stepwise approach will be followed to select studies. Data will be summarised using Microsoft Excel 2016 spreadsheet. A Newcastle-Ottawa Scale quality assessment and cochrane risk-of-bias tools will be used to assess the study’s quality. The authors will independently search databases, select studies, assess the study’s quality and extract data. Data will be analysed using STATA V.17 software. We will estimate the pooled incidence of acquired resistance with 95% CI. In addition, the pooled effect measures (OR, HR, risk ratio) with their 95% CI will be estimated. Heterogeneity will be assessed using the I 2 statistics. Publication bias will be assessed using funnel plot and Egger’s test. A subgroup analysis will be conducted for the primary outcome (acquired resistance) per each study characteristics such as WHO regional category, country’s TB/MDR-TB burden, data collection period and per the specific second-line anti-TB drug. Ethics and dissemination Since this study will be based on data extraction from published studies, ethical approval is not mandatory. The study will be published in peer-reviewed scientific journals and the findings will be presented at different scientific conferences. PROSPERO registration number CRD42022371014.
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 | 0.068 | 0.084 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.051 | 0.006 |
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".