Microbead-based technologies in diagnostic autoantibody detection
Bibliographic record
Abstract
BACKGROUND: There is a rapid proliferation of new technologies to identify a spectrum of autoantibodies in medical conditions that range from organ-specific autoimmune diseases to systemic rheumatic diseases. Although many laboratories have adopted high-throughput diagnostic platforms such as enzyme linked immunoassays (ELISA), other technologies such as microbead-based assays are emerging as an alternative diagnostic platform. OBJECTIVE: To understand the performance and importance of bead based immunoassays in clinical diagnostics and therapeutics. METHOD: Current literature was reviewed using the PubMed search engine combining keywords of immunoassay and Luminex, as well as a personal literature database. Included in the evaluation and commentary are bead-based assays such as addressable laser bead immunoassays and related magnetic bead assays. CONCLUSIONS: Comparison with other conventional technologies has indicated that laser microbead immunoassays are reliable, accurate, cost-effective, highly sensitive and have rapid turn around time for results. While there are advantages to this diagnostic platform, there are challenges that must be addressed before wider acceptance or long-term use of this technology platform in the routine clinical diagnostic laboratory.
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 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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.005 |
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".