Association between point mutations of macrolide-resistant Mycoplasma pneumoniae and clinical antibiotic treatment efficacy: a meta-analysis
Bibliographic record
Abstract
Background The increasing macrolide resistance in Mycoplasma pneumoniae is mainly driven by mutations in the V domain of 23S rRNA (A2063G/A2064G), which impairs the efficacy of first-line treatment. Previous meta-analyses failed to distinguish between mutation subtypes or quantify age-specific susceptibility, blurring the clinical significance of different mutation burdens. Objective To quantify the differential impact of single mutation (A2063G) and double mutation (A2063G + A2064G) on core clinical outcomes and to dissect the age-adjusted effects between children and adults. Methods We searched PubMed, Web of Science, Embase, Scopus, and CNKI databases (up to June 2025). The Newcastle-Ottawa Scale was used to assess study quality. Random-effects models were applied to handle heterogeneity (I 2 > 50%), and subgroup analyses were conducted to compare mutation subtypes and age-stratified effects. Results A total of 53 studies (n = 8,960 individuals, covering 5 countries) were included. Double mutations significantly prolonged the duration of fever compared to single mutations (HR = 5.32, 95% CI: 4.27–6.61 vs. HR = 3.66, 95% CI: 1.89–7.09; P < 0.001) and were more likely to cause severe illness (HR = 7.80, 95% CI: 2.51–24.18 vs. HR = 5.89, 95% CI: 2.03–17.08). There was no difference in hospital stay between the two mutation subtypes, but both were longer than the wild type (MD = −3.33 days). The duration of fever in children was shorter than that in adults for all genotypes (overall HR = 3.72 vs. 5.52; double mutation HR = 5.37 vs. 5.66; single mutation HR = 3.85 vs. 4.45; all P < 0.01). Conclusion Double mutations in 23S rRNA are an independent prognostic factor more severe than single mutations, establishing mutation burden as a key predictive indicator for the first time. This study shows that children have a faster resolution of fever in all genotypes, highlighting the regulatory role of host age immunity on outcomes. This study advocates for the detection of mutation subtypes in high-resistance areas to guide early treatment escalation and risk stratification monitoring. Systematic Review Registration https://www.crd.york.ac.uk/PROSPERO/view/CRD420251071963 , identifier CRD420251071963.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.048 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".