Correlation of platelet-related parameters and autoantibodies in patients with autoimmune bullous diseases
Bibliographic record
Abstract
INTRODUCTION: Autoimmune bullous diseases (AIBDs) are rare, tissue-specific autoimmune diseases of the skin, and corresponding autoantibodies have been proved to be pathogenic. Recently, we reported that variations in platelet-related parameters may reflect the fluctuations of circulating AIBD-correlated antibody titers during the disease progression of 1 patient with AIBD. The purpose of this article is to further investigate the possible correlation between autoantibody titers and platelet-related parameters in patients with AIBD. METHODS: This study collected data on autoantibody titers and platelet-related parameters from 136 patients with bullous pemphigoid positive for anti-BP180 antibodies, 54 patients with pemphigus foliaceus positive for anti-desmoglein (Dsg) 1 antibody, 55 patients with pemphigus vulgaris positive for both anti-Dsg1 and Dsg3 antibodies, and 16 patients with pemphigus vulgaris positive for anti-Dsg3 antibody alone. Two groups of healthy individuals served as controls. RESULTS: Comparative analyses revealed clinically significantly elevated platelet-related parameters, such as platelet count and thrombocytocrit, in the autoantibody-positive patient groups relative to control individuals. Correlation analyses demonstrated statistically significant positive associations between autoantibody titers and specific platelet-related parameters. DISCUSSION: These findings represent the first documented evidence of a positive correlation between autoantibody titers and platelet-related parameters in patients with AIBDs. The data implied that platelets may contribute to the disease pathophysiology and progression of AIBDs.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".