Autoantibodies in Common Connective Tissue Diseases: A Primer for Laboratory Professionals
Bibliographic record
Abstract
BACKGROUND: Autoimmune connective tissue disorders are a significant health concern throughout the world with an estimated prevalence of 3% to 5%. They are associated with a variety of autoantibodies that play roles in their diagnosis, risk stratification, prognostication, and/or management. While some autoantibodies have been well-characterized for use in clinical laboratories, many more are in the research stage. Rapid transition from research to clinical practice, lack of clinical guidelines, and harmonization across a rapidly growing number of commercially available tests create numerous challenges to clinicians and laboratories. CONTENT: This article briefly discusses common connective tissue disorders and their association with well-known autoantibodies, describes current methods used in clinical laboratories, and outlines their advantages and limitations in the context of these diseases. SUMMARY: Understanding the role of specific autoantibodies and various methodologies for autoantibody testing are important for laboratory professionals who may be introducing/repatriating new tests, updating existing tests, or advising clinicians/patients about testing options/results. Collaboration between laboratory professional staff and clinicians, around the advantages and limitations of each methodology, is also important in their appropriate clinical utilization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".