Late Breaking Abstract - Optimising Lung Ultrasound Criteria for ILD Screening in Rheumatoid Arthritis: A Validation Study
Bibliographic record
Abstract
Introduction: Thoracic ultrasound (TUS) is a promising screening tool for interstitial lung disease (ILD) in patients with rheumatoid arthritis (RA), but standardised protocols and diagnostic criteria remain undefined. This study aimed to validate the diagnostic performance of TUS using high-resolution computed tomography (HRCT) as the reference standard and to identify optimal TUS criteria for ILD detection. Methods: We conducted a retrospective validation study pooling data from two RA cohorts: Argentina (n=106) and Denmark (n=77). Both used a 14-zone TUS protocol, with 10 overlapping zones. In these shared zones, we evaluated total B-line count (B-li) and pleural irregularities (PI) as ILD markers. Results: Among 183 patients, ILD prevalence was 30%. A B-line count ≥6 in the 10 common zones showed the best diagnostic accuracy. Sensitivity and negative predictive value (NPV) improved when PI were present in ≥2 zones. Applying this combined criterion to the full 14-zone protocol further improved diagnostic performance. An “OR” rule combining B-li and PI increased sensitivity and reduced LR– but decreased specificity and LR+. Conclusion: Adding PI to B-line assessment enhances TUS sensitivity for ILD screening in RA, though specificity is reduced. A B-line count ≥6 using the 14-zone protocol remains the most reliable rule-out strategy. erj;66/suppl_69/PA5119/F1 F1 F1
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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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".