Use of Computer-Aided Immunofluorescence Microscopy (CAIFM) for Interpretation of Antinuclear Antibody (ANA) Pattern and Titer
Bibliographic record
Abstract
The assessment of antinuclear antibodies (ANA) by indirect immunofluorescence (IFA) allows detection of >50 characteristic autoantibodies, making it a highly sensitive method and screening test. The semiquantitative method is based on the identification of patterns at different dilutions, and each fluorescent pattern identified correlates with antibodies suggestive of autoimmune diseases. Despite being recommended as gold standard method by the American College of Rheumatology (1), IFA has unfavorable features including the need for expert morphologists, variable degree of positive titer dilution, subjectivity of pattern interpretation, and is labor intensive. To address these limitations, instrument vendors have now applied automation to the IFA technique for both the liquid handling and image interpretation. The use of automation reduces the intra- and interlaboratory variability, allows for higher throughput, and is potentially cost-effective. Currently, automation for image interpretation is present on 7 commercial systems (2), which have a range of capabilities...
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".