Perceived Functioning Has Ethnic-specific Associations in Systemic Sclerosis: Another Dimension of Personalized Medicine
Bibliographic record
Abstract
OBJECTIVE: To measure self-reported physical and mental functioning and associated clinical features at study entry in 3 ethnic groups with systemic sclerosis (SSc). METHODS: Sixty Hispanic, 39 African American, and 104 Caucasian patients with recent-onset SSc (< 5 yrs) were assessed for perceived physical and mental functioning, using the Medical Outcomes Study Short Form-36 (SF-36) and Scleroderma-Health Assessment Questionnaire (Scleroderma-HAQ). Socioeconomic, demographic, clinical, immunologic, immunogenetic, behavioral, and psychological variables (Interpersonal Support Evaluation List, ISEL; Illness Behavior Questionnaire, IBQ; and Arthritis Helplessness Index, AHI) were analyzed by linear regression models for associations with SF-36 and mHAQ scores as dependent variables. RESULTS: Perceived physical functioning scores had ethnic-specific associations with AHI > fatigue scores > IBQ > clinical variables (hypertension, skin score, and percentage predicted DLCO). Scleroderma-HAQ scores had ethnic-specific associations with IBQ > AHI scores > most clinical and laboratory variables. Decreased mental component summary (MCS) scores associated with AHI > ISEL. Ethnic-specific immunogenetic variables HLA-DQB1*0202 (Caucasian) and HLA-DRB 1*11 (African American), and HLA-DQA1*0501 (Hispanic) also associated with MCS. Antinuclear autoantibodies, anti-topoisomerase I, and RNA polymerases I and III also demonstrated associations with functioning in African American and Hispanic groups. CONCLUSION: Clinical, psychosocial, and immunogenetic variables had ethnic-specific associations with perceived physical and mental functioning. Consideration of ethnic-specific psychological and behavioral support in designing more personalized, relevant therapeutic interventions for the patient may improve therapeutic efficacy in 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".