Dermatologic ultrasound in the management of childhood linear morphea
Bibliographic record
Abstract
Linear morphea is the most common subtype of localized scleroderma in the pediatric population. This condition can be quite disabling, with complications such as growth defects and painful flexion contractures. Assessment of disease progression and early intervention are key to minimize morbidity. We report linear morphea in a previously healthy 12-year-old girl. The patient presented with a one-year history of a linear plaque crossing her left antecubital fossa, measuring 7x3cm. The diagnosis was confirmed by biopsy, in which deep tissue involvement was noted. Subsequent management and evaluation of the disease activity was done by ultrasound, which allowed precise guidance of pharmacotherapy. The patient improved both clinically and sonographically with a methotrexate course. Sonographic changes accurately described the disease activity on follow up assessments. Features suggestive of an active phase include a thickened and hypoechoic dermis contrasting hyperechoic subcutaneous tissue. The atrophic stage is characterized by a thinned-out dermis and subcutaneous area. Typical vascular traits of each disease phase can also contribute to the assessment. Ultrasound is a grossly underused tool in the field of dermatology. It can provide accurate and sensitive information about disease activity in linear morphea, allowing for more timely intervention and optimal patient management.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".