Toward a new autoantibody diagnostic orthodoxy: understanding the bad, good and indifferent
Bibliographic record
Abstract
Rapid advances in diagnostic technologies used to detect autoantibodies have made it difficult for even the most modern laboratory to keep abreast of the changing approaches and platforms, not to mention the clinicians who are hard pressed to keep abreast of evolving diagnostic paradigms attended by these newer techniques. While autoantibody testing is traditionally considered to be primarily serving the realm of diagnostic medicine, there is little doubt that autoantibodies are also being recognized as an approach to providing prognostic and therapeutic information. Accordingly, along with related proteomics, genomics and metabolomics, it is taking on increasing importance in the realm of personalized medicine. In today's world of autoantibody diagnostics, overarching concerns about false-negative and false-positive autoantibodies tests cannot be summarily dismissed by citing pros or cons of any one technology or diagnostic platform, but often point to persisting gaps in our knowledge about, and understanding of, the origin and roles of autoantibodies. Before we can hope to completely understand the enigmas that attend the results of autoantibody diagnostic tests, perhaps it is time to step back and re-examine long-accepted paradigms and beliefs. This review will address some of the issues that impact on autoantibody detection technologies and some of the considerations and issues that will attend a new orthodoxy of autoantibody diagnostics. These issues will be addressed in the context of "bad" (pathogenic), "good" (protective) or "indifferent" (no apparent role in disease) autoantibodies.
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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 | 0.022 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.020 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".