High-frequency Ultrasound Assessment of Systemic Sclerosis Skin Involvement: Intraobserver Repeatability and Relationship With Clinician Assessment and Dermal Collagen Content
Bibliographic record
Abstract
OBJECTIVE: The modified Rodnan skin score (mRSS) remains the preferred method for skin assessment in systemic sclerosis (SSc). There are concerns regarding high interobserver variability of mRSS and negative clinical trials utilizing mRSS as the primary endpoint. High-frequency ultrasound (HFUS) allows objective assessment of cutaneous fibrosis in SSc. We investigated the relationship between HFUS with both mRSS and dermal collagen. METHODS: Skin thickness (ST), echogenicity, and novel shear wave elastography (SWE) were assessed in 53 patients with SSc and 15 healthy controls (HCs) at the finger, hand, forearm, and abdomen. The relationship between HFUS parameters with mRSS (n = 53) and dermal collagen (10 patients with SSc and 10 HCs) was investigated. Intraobserver repeatability of HFUS was calculated using intraclass correlation coefficients (ICCs). RESULTS: HFUS assessment of ST (hand/forearm) and SWE (finger/hand) correlated with local mRSS at some sites. Subclinical abnormalities in ST, echogenicity, and SWE were present in clinically uninvolved SSc skin. Additionally, changes in echogenicity and SWE were sometimes apparent despite objectively normal ST on HFUS. ST, SWE, and local mRSS correlated strongly with collagen quantification (r = 0.697, 0.709, 0.649, respectively). Intraobserver repeatability was high for all HFUS parameters (ICCs for ST = 0.946-0.978; echogenicity = 0.648-0.865; and SWE = 0.953-0.973). CONCLUSION: Our data demonstrate excellent reproducibility and reassuring convergent validity with dermal collagen content. Detection of subclinical abnormalities is an additional benefit of HFUS. The observed correlations with collagen quantification support further investigation of HFUS as an alternative to mRSS in clinical trial settings.
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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 | 0.011 | 0.026 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".