Perifollicular Hypopigmentation in Systemic Sclerosis: Associations With Clinical Features and Internal Organ Involvement
Bibliographic record
Abstract
Objective To determine whether perifollicular hypopigmentation in systemic sclerosis (SSc) is associated with demographics, distinct clinical features, and autoantibody profiles. Methods Patients with SSc were prospectively enrolled, with a standardized data form used to collect anatomic distribution of perifollicular hypopigmentation. Associations between hypopigmentation and features of SSc were assessed. Results Of 179 adult patients with SSc, 36 (20%) patients had perifollicular hypopigmentation. Of these 36 patients, 94% (n = 34) were female and 33% (n = 12) had limited cutaneous SSc. In univariable logistic regression, Black race (odds ratio [OR] 15.63, 95% CI 6.6–37.20,P< 0.001), diffuse cutaneous SSc (dcSSc; OR 4.62, 95% CI 2.11–10.09,P< 0.001), higher maximum modified Rodnan skin score (mRSS; OR 1.05, 95% CI 1.02–1.08,P= 0.003), myopathy (OR 3.92, 95% CI 1.80–8.57,P< 0.001), pulmonary fibrosis (OR 2.69, 95% CI 1.20-6.02,P= 0.02), lower minimum forced vital capacity % predicted (OR 0.96, 95% CI 0.94–0.99,P= 0.001), and lower minimum diffusing capacity for carbon monoxide % predicted (OR 0.97, 95% CI 0.95–0.99,P= 0.009) were associated with hypopigmentation. Anticentromere antibodies inversely associated with hypopigmentation (OR 0.24, 95% CI 0.07–0.86,P= 0.03). After adjusting for age, race, and disease duration, dcSSc (OR 4.28, 95% CI 1.46-12.53,P= 0.008) and increased mRSS (OR 1.07, 95% CI 1.02–1.12,P= 0.009) were significantly associated with hypopigmentation. Conclusion Perifollicular hypopigmentation is observed in a subset of patients with SSc and associated with diffuse subtype. Larger prospective studies determining whether perifollicular hypopigmentation precedes end-organ involvement and whether specific patterns associate with internal organ involvement are needed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".