A Systematic Review and Meta-Analysis of Sputum Purulence to Predict Bacterial Infection in COPD Exacerbations
Bibliographic record
Abstract
The 2020 Global Initiative for Obstructive Lung Disease (GOLD) Report highlights the importance of sputum purulence in the decision to prescribe antibiotics for acute exacerbations. The purpose of this systematic review and meta-analysis was to evaluate the strength of literature supporting inclusion of sputum purulence in criteria utilized to evaluate if antimicrobials are indicated in acute COPD exacerbation. A total of 6 observational studies met inclusion criteria for this meta-analysis. Sputum purulence was defined by visual assessment of color, either subjectively by providers and/or patients or by a colored chart, where green or yellow sputum was considered purulent. Four of the studies were primarily conducted in hospitalized patients, one in the emergency department, and one in the primary care setting. Five studies relied upon expectorated sputum and one used bronchoscopy to obtain sputum samples for bacterial cultures. Compared with mucoid sputum, purulent sputum had a significantly higher probability of positive bacterial culture results (RR = 2.14, 95%CI [1.25, 3.67], p = 0.006, moderate quality). For sensitivity analysis, after removal of studies losing 2 or more points from the New Castle-Ottawa scale, the effect value remained statistically significant. This systematic review and meta-analysis showed a moderate level of evidence that purulent sputum during COPD exacerbation, as defined by yellow or green color, is associated with a significantly higher probability of potentially pathogenic bacteria, supporting GOLD report and NICE recommendations.
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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 | 0.018 | 0.056 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.043 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".