Evaluation of the relationship between salivary concentration of anti-heat shock protein immunoglobulin and clinical manifestations of Behçet’s disease
Bibliographic record
Abstract
OBJECTIVE: This study investigated the relationship between the concentration of anti-heat shock protein (HSP) 60 antibody in resting saliva and the severity of Behçet's disease (BD). METHOD: Sixty-five patients diagnosed with BD at Tokyo Medical and Dental University Hospital were enrolled in this study. Based on clinical severity scores, patients were categorized as having mild, moderate, or severe BD. Periodontal status was evaluated with the Community Periodontal Index (CPI), and anti-HSP60 antibody concentrations in resting saliva were measured with an enzyme-linked immunosorbent assay. RESULTS: The mean antibody concentration in patients in the moderate group was significantly higher than concentrations in the mild and severe groups. No significant difference was found between the mild and severe groups. Gingival inflammation, identified with the CPI, was associated with a higher antibody concentration. The antibody concentration in patients who had stomatitis for more than 2 weeks was significantly higher than in those with stomatitis for less than 2 weeks. The antibody concentration in patients who had taken colchicine was significantly lower than that in subjects who had not. CONCLUSION: These results suggest that the concentration of anti-HSP60 antibody in resting saliva may be effective as a non-invasive indicator for the diagnosis (screening) and prognostication of BD.
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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 | 0.002 |
| 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) | 0.001 | 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".