A.6 The clinical validation of a comprehensive neural autoantibodies testing
Bibliographic record
Abstract
Background: New neural antibodies are being identified each year and determining how to measure them and how to interpret test results is complex. In addition, screening with two methods is recommended for most antibodies, particularly paraneoplastic antibodies. We report the clinical validation and profile of a series of neural autoantibodies detected with a comprehensive testing algorithm. Methods: This is an ongoing study in which we are asking for the clinical correlation and final diagnosis of patients whose serum and/or CSF samples were tested at the BC Neuroimmunology Lab, Vancouver for neural autoantibodies. We performed immunofluorescence screening assay/IHC in rat brain sections in combination with confirmatory fixed or live Cell-Based assays and/or immunoblots. Results: We obtained clinical information from 219 samples (22 positive), Upon clinical inquiry, we obtained clinical information on 12 cases (five positive and seven negative). One Ttitin positive case was associated with anti-acetylcholine receptor antibody myasthenia and one Zic4 antibody was detected as a false positive by immunoblot but was negative by Rat Brain IHC. Conclusions: We have identified 10 percent seropositivity on 219 samples testing for Mosai-6 and full paraneoplastic testing. Further clinical validation studies are ongoing to evaluate the accuracy of our serological testing for neural antibodies.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".