Meta-analysis of bacterial resistance to macrolides
Bibliographic record
Abstract
OBJECTIVES: Understanding changing resistance patterns is important in determining appropriate antibiotic treatments. This meta-analysis systematically evaluated resistance of Streptococcus pneumoniae and Streptococcus pyogenes to macrolide antibiotics among patients with community-acquired respiratory tract infections. METHODS: MEDLINE and EMBASE databases were searched and experts were consulted to identify published and unpublished literature reporting macrolide resistance rates. Identified studies were evaluated by two independent reviewers; those meeting a priori specified criteria (resistance by patient condition and strain, resistance thresholds, 1997-2003 isolates) were included. Data from included studies were abstracted by two independent reviewers using a standard review form. Discrepancies in abstracted data were resolved by the study investigator. RESULTS: Random-effects meta-analysis was performed for outcomes present in at least four studies overall and for specified subgroups. We identified 3849 studies and performed detailed review on 407; of these 29, published between 1998-2003, met the inclusion criteria. Mean resistance of S. pneumoniae isolates to azithromycin was 27.2% [95% confidence interval (CI) 24.6-29.7]; mean resistance to erythromycin was statistically equivalent (30.4%; 95% CI 28.1-32.7). Resistance of S. pyogenes to erythromycin (30.0%; CI 18.6-41.5) was similar to that of S. pneumoniae. Too few studies of clarithromycin were included to allow evaluation of resistance. In subgroup analyses, substantial variation in resistance to erythromycin was seen by geographic area. CONCLUSIONS: Reported macrolide resistance of S. pneumoniae varies substantially and may be a significant issue in certain regions. Use of meta-analysis to aggregate individual studies enabled determination of robust values for macrolide resistance. This information is useful for clinical and policy decision makers in developing appropriate antibiotic strategies.
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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 | 0.027 | 0.050 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.069 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".