Ultrasound in the Assessment of Pulmonary Fibrosis in Connective Tissue Disorders: Correlation with High-Resolution Computed Tomography
Bibliographic record
Abstract
OBJECTIVE: To investigate the correlation between ultrasound (US) B-lines and high-resolution computed tomography (HRCT) findings in the assessment of pulmonary fibrosis (PF) in patients with connective tissue disorders (CTD). METHODS: Thirty-four patients with a diagnosis of CTD were included. Each patient underwent clinical examination, pulmonary function test (PFT), chest HRCT, and lung US by an experienced radiologist or rheumatologist. A second rheumatologist carried out US examinations to assess interobserver agreement. In each patient, US B-line lung assessment including 50 intercostal spaces (IS) was performed. For the anterior and lateral chest, the IS were the second to the fifth along the parasternal, mid-clavicular, anterior axillary, and medial axillary lines (the left fifth IS of the anterior and lateral chest was not performed because of the presence of the heart, which limits lung visualization). For the posterior chest, the IS assessed were the seventh to the eighth along the posterior-axillary and subscapular lines. The second to eighth IS were assessed in the paravertebral line. In each IS, the number of US B-lines under the transducer was recorded, summed, and graded according to the following semiquantitative scoring: grade 0 = normal (< 10 B-lines); grade 1 = mild (11 to 20 B-lines); grade 2 = moderate (21 to 50 B-lines); and grade 3 = marked (> 50 B-lines). RESULTS: A total of 1700 IS in 34 patients were assessed. A significant linear correlation was found between the US score and the HRCT score (p < 0.001; correlation coefficient ρ = 0.875). A positive correlation was found between US B-line assessments and values of DLCO (p = 0.014). Both κ values and overall percentages of interobserver agreement showed excellent agreement. CONCLUSION: Our study demonstrates that US B-line assessment may be a useful and reliable additional imaging method in the evaluation of PF in patients with CTD.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".