HLA markers for susceptibility and expression in scleroderma.
Bibliographic record
Abstract
OBJECTIVE: Reported associations between HLA alleles and both susceptibility to and features of scleroderma have been conflicting. Our objective was (1) to determine the role of HLA alleles in the susceptibility to scleroderma; and (2) to determine the role of HLA alleles in various aspects of disease expression. METHODS: Consecutive patients were followed in the scleroderma clinic between 1996 and 1998. Clinical data were obtained through chart review. Healthy volunteers as well as cadaveric donors served as controls. Molecular HLA typing was performed (polymerase chain reaction/sequence-specific oligonucleotides). Statistical analysis included Fisher's exact test and multivariate analyses, using logistic and linear regression models. RESULTS: Ninety-five Caucasian patients (75 women, 20 men, age 43.9 yrs, disease duration 11.9 yrs) with scleroderma and 416 controls were studied. HLA-DRB1*01 and HLA-DRB1*11 were associated with susceptibility to scleroderma, whereas HLA-DRB1*07 was protective. HLA-A*30 and HLA-A*32 were also associated with susceptibility to scleroderma, while HLA-B*57 and HLA-Cw*14 were protective. HLA-B*62 and HLA-DRB1*07 had a significant correlation with the presence of diffuse skin involvement in both univariate and multivariate analyses. HLA-DRB1*11 was associated with high skin score values, while lower values were related to the presence of HLA-Cw*14 and HLA-DQB1*06. Both alleles retained significance in a linear regression model. High skin score values were related to the absence of anticentromere antibodies. Pulmonary fibrosis was associated with HLA-B*62 and HLA-Cw*0602, whereas pulmonary hypertension was associated with HLA-B*13 and HLA-B*65. CONCLUSION: HLA alleles play a role in susceptibility to scleroderma and its disease expression.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".