Ultrasound in the Assessment of Interstitial Lung Disease in Systemic Sclerosis: A Systematic Literature Review by the OMERACT Ultrasound Group
Bibliographic record
Abstract
OBJECTIVE: To provide an overview of the role of lung ultrasound (LUS) in the assessment of interstitial lung disease (ILD) in systemic sclerosis (SSc) and to discuss the state of validation supporting its clinical relevance and application in daily clinical practice. METHODS: Original articles published between January 1997 and October 2017 were included. To identify all available studies, a detailed search pertaining to the topic of review was conducted according to guidelines of the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA). A systematic search was performed in PubMed and EMBASE. The quality assessment of retrieved articles was performed according to the Oxford Center for Evidence-based Medicine. The methodological quality of the studies was assessed using the Cochrane Handbook for Systematic Reviews and the Quality Assessment of Diagnostic Accuracy Studies-2 tool. RESULTS: From 300 papers identified, 12 were included for the analysis. LUS passed the filter of face, content validity, and feasibility. However, there is insufficient evidence to support criterion validity, reliability, and sensitivity to change. CONCLUSION: Despite a great deal of work supporting the potential role of LUS for the assessment of ILD-SSc, much remains to be done before validating its use as an outcome measure in ILD-SSc.
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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 | 0.009 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.018 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".