Sputum colour and bacteria in chronic bronchitis exacerbations: a pooled analysis
Bibliographic record
Abstract
We examined the correlation between sputum colour and the presence of potentially pathogenic bacteria in acute exacerbations of chronic bronchitis (AECBs). Data were pooled from six multicentre studies comparing moxifloxacin with other antimicrobials in patients with an AECB. Sputum was collected before antimicrobial therapy, and bacteria were identified by culture and Gram staining. Association between sputum colour and bacteria was determined using logistic regression. Of 4,089 sputum samples, a colour was reported in 4,003; 1,898 (46.4%) were culture-positive. Green or yellow sputum samples were most likely to yield bacteria (58.9% and 45.5% of samples, respectively), compared with 18% of clear and 39% of rust-coloured samples positive for potentially pathogenic microorganisms. Factors predicting a positive culture were sputum colour (the strongest predictor), sputum purulence, increased dyspnoea, male sex and absence of fever. Green or yellow versus white sputum colour was associated with a sensitivity of 94.7% and a specificity of 15% for the presence of bacteria. Sputum colour, particularly green and yellow, was a stronger predictor of potentially pathogenic bacteria than sputum purulence and increased dyspnoea in AECB patients. However, it does not necessarily predict the need for antibiotic treatment in all patients with AECB.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.015 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".