Carbapenem-resistant Enterobacterales/Enterobacteriaceae (CRE) in Indonesia: protocol for systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Carbapenem-resistant Enterobacterales (CRE) pose a significant global health threat, with increasing prevalence worldwide, including in Indonesia. Despite the public health impact, comprehensive data on the burden of CRE in Indonesia remains fragmented. This protocol outlines a systematic review and meta-analysis aiming to estimate the prevalence of CRE in Indonesia, summarize trends over time, and identify key resistance mechanisms. Methods We will conduct a systematic search across multiple electronic databases, including PubMed, Scopus, and local Indonesian databases, for studies reporting the prevalence of CRE in Indonesia from 2008 to 2024. Eligibility criteria include observational studies (cross-sectional, cohort, and case-control) and surveillance reports. Data extraction will focus on CRE prevalence, bacterial species, sample types, resistance mechanisms, and study settings (hospital vs. community). Quality assessment of studies will be performed using the Newcastle-Ottawa Scale (NOS). Meta-analysis will be conducted using a random-effects model to estimate pooled prevalence, and subgroup analysis will explore variations by geographical region, period, and healthcare setting. Discussion This systematic review and meta-analysis will provide the first comprehensive overview of CRE prevalence in Indonesia, contributing to an improved understanding of the national burden and resistance patterns. The findings will guide public health policies and inform antimicrobial stewardship efforts in Indonesia. Registration PROSPERO CRD42024580177
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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 | 0.057 | 0.088 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.078 | 0.007 |
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".