Biomarkers in atypical pneumonia: a systematic review of diagnostic and prognostic utility
Bibliographic record
Abstract
Atypical pneumonia, driven by pathogens like Mycoplasma pneumoniae, Chlamydia pneumoniae, and Legionella pneumophila, is challenging to diagnose due to non-specific symptoms. This systematic review assessed the diagnostic accuracy and prognostic value of biomarkers in atypical pneumonia. A comprehensive search of PubMed, Scopus, Web of Science, and Google Scholar (2000-2024) identified 27 studies, including observational, cohort, case-control, and review designs. Studies focused on biomarkers such as C-reactive protein (CRP), procalcitonin (PCT), ferritin, D-dimer, and pathogen-specific antibodies, with quality evaluated using the Newcastle-Ottawa Scale and AMSTAR 2. CRP was elevated in 85% of cases, with a pooled sensitivity of 82.3% [95% confidence interval (CI) 76.5-88.1, I²=78%] but moderate specificity (65.2%, 95% CI 58.0-72.4). PCT exhibited high specificity (88.7%, 95% CI 83.2-94.2, I²=65%) for bacterial etiologies, making it valuable for distinguishing bacterial from viral infections. Anti-Mycoplasma pneumoniae immunoglobulin M (IgM) showed excellent diagnostic accuracy (sensitivity 90.1%, 95% CI 85.0-95.2). Ferritin levels >400 ng/mL were strongly associated with severe outcomes [odds ratio (OR) 3.15, 95% CI 2.10-4.72, I²=70%]. Elevated biomarkers correlated with increased hospitalization (OR 2.78, 95% CI 1.95-3.96) and mortality (OR 3.42, 95% CI 2.30-5.08). Heterogeneity was significant (I²=65-78%), reflecting variability in study populations and methods. PCT and anti-Mycoplasma pneumoniae IgM enhance diagnostic precision, while ferritin and CRP are robust prognostic markers. Standardized biomarker thresholds are essential to optimize their clinical utility and improve patient outcomes in atypical pneumonia management.
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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 | 0.010 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".