Prevalence and Incidence Antimicrobial Resistance of Patients Pneumonia in Indonesia: Systematic Review and Meta-analysis
Bibliographic record
Abstract
A BSTRACT Introduction: Antimicrobial resistance (AMR) remains a threat in Indonesia. In this regard, AMR also poses a challenge in the management of pneumonia. This systematic review and meta-analysis aimed to evaluate the prevalence and AMR of pneumonia-causing microorganisms in Indonesia, with a focus on Gram-negative and Gram-positive bacteria. Methods: We searched five databases and screened 997 studies. After removing duplicates and irrelevant studies, eight studies (six retrospective and two cross-sectional) were included in the study. Data on bacterial prevalence and antibiotic resistance were extracted, and bias assessment was performed using the Newcastle–Ottawa Scale and Agency for Healthcare Research and Quality standards. Meta-analysis of proportion was carried out using R version 4.4 with the random-effect model. Results: The pooled prevalence of Gram-negative bacteria was higher than Gram-positive. Klebsiella pneumoniae had the highest prevalence of 0.21 (95% confidence interval [CI]: 0.16–0.27), followed by Acinetobacter baumannii 0.20 (95% CI: 0.13–0.26) and Pseudomonas aeruginosa 0.12 (95% CI: 0.09–0.15). Staphylococcus aureus and Streptococcus pneumoniae had the highest Gram-positive prevalence of 0.07 each. K . pneumoniae and A . baumannii exhibited high resistance to cephalosporins but retained sensitivity to carbapenems and amikacin. Conclusions: Gram-negative bacteria are prevalent among pneumonia patients in Indonesia, showing high resistance to cephalosporins. Further research and antimicrobial stewardship programs are needed to curb rising AMR rates.
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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 | 0.012 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.040 |
| Bibliometrics | 0.009 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".