Multiplex planar microarrays for disease prognosis, diagnosis and theranosis
Bibliographic record
Abstract
Advanced diagnostic methods and algorithms for immune disorders provide qualitative and quantitative multiplex measurement for pre-clinical prognostic and clinical diagnostic biomarkers specific for diseases. Choice of therapy is confirmed by modulating diagnostic efficacy of companion, theranotic drug concentrations. Assay methods identify, monitor and manage autoimmune diseases, or risk thereof, in subjects who have, or who are related to individuals with autoimmune disease. These same diagnostic protocols also integrate qualitative and quantitative assay test protocol designs for responder patient assessment, risk analysis and management of disease when integrating multiplex planar microarray diagnostic tests, patient theranostic companion diagnostic methods and test panels for simultaneous assessment and management of dysimmune and inflammatory disorders, autoimmunity, allergy and cancer. Proprietary assay methods are provided to identify, monitor and manage dysimmune conditions, or risk thereof, in subjects with pathological alterations in the immune system, or who are related to individuals with these conditions. The protocols can be used for confirmatory testing of subjects who exhibit symptoms of dysimmunity, as well as subjects who are apparently healthy and do not exhibit symptoms of altered immune function. The protocols also provide for methods of determining whether a subject has, is at risk for, or is a candidate for disease therapy, guided by companion diagnosis and immunosuppressive therapy, as well as therapeutic drug monitoring and theranostic testing of disease biomarkers in response to immuno-absorption therapy. The multiplex test panels provide the components that are integral for performing the methods to recognized clinical standards.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".