Advances and Applications of Multiplexed Diagnostic Technologies in Autoimmune Diseases
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
There is a rapid proliferation of new technologies to identify an ever increasing spectrum of autoantibodies in diverse medical conditions that range from organ-specific autoimmune diseases to systemic rheumatic diseases. Although many laboratories have adopted diagnostic platforms, such as enzyme linked immunoassays (ELISAs), to improve turn around times and meet budget constraints, the prevailing evidence is that the rapid adoption of new technologies is not attended by an appropriate balance of assay sensitivity and specificity. Emerging diagnostic technologies include addressable laser bead immunoassays, microarrays in microfluidics platforms and nanobarcode particles. Although these technologies provide advantages of high-throughput, multiplexed autoantibody assays that can be coupled to other disease specific biomarkers (ie, cytokines, single nucleotide polymorphisms) there is a clear need for standardization and internal validation before they are adopted into the 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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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