Therapeutic implications of macrolide resistance in pneumococcal community-acquired lower respiratory tract infections
Bibliographic record
Abstract
Macrolide anti-bacterials are widely used for the empirical treatment of lower respiratory tract infections (RTIs) due to their activity against Streptococcus pneumoniae and other common respiratory pathogens and good safety/tolerability profile. However, the prevalence of macrolide resistance, particularly pneumococcal macrolide resistance, is increasing all around the world. The mechanisms underlying macrolide resistance include efflux pump, methylase activity and, less commonly, ribosomal mutation, which produce differing levels of resistance. Growth in macrolide resistance has been linked to the increased use of these agents, and several risk factors for the development of resistance have been identified. There are emerging data to suggest that in vitro macrolide resistance may increase the likelihood of treatment failure in patients with lower RTIs. However, at present, treatment failure is rare and randomised; intervention-based trials investigating the impact of anti-bacterial resistance on clinical outcomes are lacking. Strategies to promote appropriate use of macrolides and other anti-bacterials are needed, both to maximise therapeutic impact and to minimise the development of resistance. Furthermore, there is a need for alternative anti-bacterial agents which have high efficacy against respiratory pathogens (including resistant strains) and a low potential to induce resistance.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".