Determinants of bacteriological outcomes in exacerbations of chronic obstructive pulmonary disease
Bibliographic record
Abstract
PURPOSE: Changes in sputum microbiology following antibiotic treatment of acute exacerbations of chronic obstructive pulmonary disease (AECOPD), including patterns of bacteriological relapse and superinfection are not well understood. Sputum microbiology at exacerbation is not routinely performed, but pathogen presence and species are determinants of outcomes. Therefore, we determined whether baseline clinical factors could predict the presence of bacterial pathogens at exacerbation. Bacterial eradication at end of treatment (EOT) is associated with clinical resolution of exacerbation. We determined the clinical, microbiological and therapeutic factors that were associated with bacteriological eradication in AECOPD at EOT and in the following 8 weeks. METHODS: Sputum bacteriological outcomes (i.e., eradication, persistence, superinfection, reinfection) from AECOPD patients (N = 1352) who were randomized to receive moxifloxacin or amoxicillin/clavulanate in the MAESTRAL study were compared. Independent predictors of bacterial presence in sputum at exacerbation and determinants for bacteriological eradication were analyzed by logistic regression and receiver operating characteristic (ROC) analyses. RESULTS: Significantly greater bacteriological eradication with moxifloxacin was mainly driven by superior Haemophilus influenzae eradication (P = 0.002, EOT). Baseline clinical factors were a weak predictor of the presence of pathogens in sputum (AUCROC = 0.593). On multivariate analysis, poorer bacterial eradication was associated with antibiotic resistance (P = 0.0001), systemic steroid use (P = 0.0024) and presence of P. aeruginosa (P = 0.0282). CONCLUSIONS: Since clinical prediction of bacterial presence in sputum at AECOPD is poor, sputum microbiological analysis should be considered for guiding antibiotic therapy in moderate-to-severe AECOPD, particularly in those who received concomitant systemic corticosteroids or are at risk for infection with antibiotic-resistant bacteria.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".