Ultrasound Elastography Assessment of Skin Involvement in Systemic Sclerosis: Lights and Shadows
Bibliographic record
Abstract
OBJECTIVE: To assess skin elasticity in systemic sclerosis (SSc) by using a new imaging modality, ultrasound elastography (UE). METHODS: Our study included 18 consecutive patients with SSc and 15 healthy controls. Modified Rodnan skin score, physical examination, and assessment of organ involvement were performed. UE was carried out on the middle forearm and on the fingers of the dominant arm. The echo signals recorded in real time during freehand operations of probe compression and relaxation produced images representing tissue elasticity, consisting of translucent colored bands superimposed on the B-mode ultrasonographic images. The color scale varied within a large band spectrum from red, indicative of soft and highly elastic tissue, to blue, which denoted hard and barely elastic tissue. RESULTS: On the forearm of all patients, UE showed a homogeneous blue area corresponding to the dermis visualized in a B-mode ultrasonographic image; in controls, a blue pattern was never detected and a predominance of green with sporadic areas of pale blue was observed. At sequential evaluations, UE of fingers produced inconstant and changeable colored areas. CONCLUSION: The imaging pattern observed in the forearm of patients with SSc may represent the reduction of strain in the dermis due to loss of elasticity. The variable pattern obtained by finger evaluation demonstrated that UE can assess skin involvement in SSc only in those areas where the dermis is known to be thicker and where the bone hyperreflection is minimal. Further studies are needed to confirm our results and determine the validity of this new imaging modality.
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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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".