A Preliminary Study of Acoustic Radiation Force Impulse Quantification for the Assessment of Skin in Diffuse Cutaneous Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To investigate skin elasticity using acoustic radiation force impulse (ARFI) quantification in systemic sclerosis (SSc), and compare the modified Rodnan skin score (mRSS) with measured shear wave velocity (SWV) and thickness of the skin. METHODS: Fifteen patients with diffuse cutaneous SSc (dcSSc) and 15 age-matched and sex-matched healthy controls were evaluated. The SWV and thickness of skin were measured at 17 sites corresponding to those assessed in the mRSS in each participant. The SWV measurements of skin were compared between patients with dcSSc and healthy controls. The correlations between the mRSS and the skin SWV and thickness were explored using Spearman's correlation. RESULTS: The SWV values were higher in patients with dcSSc compared with healthy controls at right hand dorsum, right forearm, left hand dorsum, left forearm, right foot dorsum, and left foot dorsum (p < 0.05). In patients with dcSSc, the SWV values of uninvolved skin were higher than those of controls (p < 0.001), and the SWV values increased with increasing skin scores except for skin score 3 (p < 0.05). The sum of the SWV values correlated with total clinical skin score (r = 0.841, p < 0.001), and the sum of the skin thickness correlated with total clinical skin score (r = 0.740, p = 0.002). CONCLUSION: ARFI quantification is feasible and reliable for assessing the skin involvement in dcSSc. ARFI quantification could identify early skin change that may precede palpable skin involvement, and may be a valuable adjunct to skin evaluation in patients with 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".