Detection of autoantibodies using chemiluminescence technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
CONTEXT: Although autoantibody detection methods such as indirect immunofluorescence (IIF) and enzyme-linked immunosorbent assays (ELISAs) have been available for many years and are still in use the innovation of fast, fully automated instruments using chemiluminescence technology in recent years has led to rapid adoption in autoimmune disease diagnostics. In 2009, BIO-FLASH, a fully automated, random access chemiluminescent analyzer, was introduced, proceeded by the development of the QUANTA Flash chemiluminescent immunoassays (CIA) for autoimmune diagnostics. OBJECTIVE: To summarize the evolution of CIAs for the detection of autoantibodies and to review their performance characteristics. METHODS: Pubmed was screened for publications evaluating novel QUANTA Flash assays and how they compare to traditional methods for the detection of autoantibodies. In addition, comparative studies presented at scientific meetings were summarized. RESULTS: Several studies were identified that compared the novel CIAs with conventional methods for autoantibody detection. The agreements ranged from moderate to excellent depending on the assay. The studies show how the CIA technology has enhanced the analytical and clinical performance characteristics of many autoantibody assays supporting both diagnosis and follow-up testing. CONCLUSION: CIA has started to improve the diagnostic testing of autoantibodies as an aid in the diagnosis of a broad range of autoimmune diseases.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 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 it