Clinical cure rates in patients treated with azithromycin (AZ) for lower respiratory infections (LRTI) caused by AZ-susceptible (AZ-S) and AZ-resistant (AZ-R) organisms: Analysis of phase 3 clinical trials
Bibliographic record
Abstract
Introduction: LRTI are commonly seen in respiratory medicine and include both exacerbations of COPD and community acquired pneumonia. They are often empirically treated with macrolides such as AZ. Resistance rates to macrolides are rising in many parts of the world. Aim: The aim was to determine cure rates in AZ-treated patients with LRTI due to AZ-S and AZ-R organisms in randomized clinical trials (RCT). Methods: 543 patients received AZ in 9 Phase 3 RCTs (1993-2007). Low-level AZ resistance (LLAR) was AZ MIC ≤8 μg/ml; high-level AZ resistance (HLAR) was AZ MIC ≥16μg/ml. Results: 543 LRTI patients had 718 isolates; 174 had an AZ-R organism; 131 had ≥2 pathogens. Cure rates were: 93.4% (507/543) for all LRTI patients; 95.4% (352/369) for AZ-S patients; 89.1% (155/174) for AZ-R patients; 90.7% (97/107) for LLAR and 86.6% (58/67) for HLAR isolates; and 93.9% (123/131) in patients with multiple isolates. Conclusion: AZ cure rates were higher in patients with only AZ-S isolates (95.4%) vs ≥1 AZ-R isolates (89.1%); p=0.006. However, interestingly, no difference was observed for LLAR vs HLAR (p=0.40). AZ is actually an azalide, and is concentrated within lysosomes within circulating white blood cells. Thus serum is not the reservoir supplying tissues and this may underlie the weak relationship between MIC and cure rate. Despite rising resistance to AZ, cure rates are still substantial allowing empirical treatment with AZ to be appropriate for many patients with LRTI.
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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 | 0.017 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".