Point-of-care lung ultrasound in detecting pneumonia: A systematic review
Bibliographic record
Abstract
Purpose: Limited evidence exists to assess the sensitivity, specificity, and accuracy of point-of-care lung ultrasound (LUS) across all age groups. This review aimed to investigate the benefits of point-of-care LUS for the early diagnosis of pneumonia compared to traditional chest X-rays (CXR) in a subgroup analysis including pediatric, adult, and geriatric populations. Material and Methods: This systematic review examined systematic reviews, meta-analyses, and original research from 2017 to 2021, comparing point-of-care LUS and CXR in diagnosing pneumonia among adults, pediatrics and geriatrics. Studies lacking direct comparison or exploring diseases other than pneumonia, case reports, and those examining pneumonia secondary to COVID-19 variants were excluded. The search utilized PubMed, Google Scholar, and Cochrane databases with specific search strings. The study selection, conducted by two independent investigators, demonstrated an agreement by the Kappa index, ensuring reliable article selection. The QUADAS-2 tool assessed the selected studies for quality, highlighting risk of bias and applicability concerns across key domains. Statistical analysis using Stata Version 16 determined pooled sensitivity and specificity via a bivariate model, emphasizing LUS and CXR diagnostic capabilities. Additionally, RevMan 5.4.1 facilitated the calculation of sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV), offering insights into diagnostic accuracy. Results: The search, conducted across PubMed, Google Scholar, and Cochrane Library databases by two independent investigators, initially identified 1045 articles. Following screening processes, 12 studies comprised a sample size of 2897. LUS demonstrated a likelihood ratio of 5.09, a specificity of 81.91%, and a sensitivity of 92.13% in detecting pneumonia in pediatric, adult, and geriatric patients, with a p-value of 0.0002 and a 95% confidence interval, indicating diagnostic accuracy ranging from 84.07% to 96.29% when compared directly to CXR. Conclusion: Our review supports that LUS can play a valuable role in detecting pneumonia early with high sensitivity, specificity, and diagnostic accuracy across diverse patient demographics, including pediatric, adult, and geriatric populations. Since it overcomes most of the limitations of CXR and other diagnostic modalities, it can be utilized as a diagnostic tool for pneumonia for all age groups as it is a safe, readily available, and cost-effective modality that can be utilized in an emergency department, intensive care units, wards, and clinics by trained respiratory care professionals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".