Mass spectrometry in IgG4-related disease diagnosis
Bibliographic record
Abstract
We compared liquid chromatography tandem mass spectrometry (LC-MS/MS) against Binding Site immunonephelometry (BSIN) with regards to these methods' abilities to diagnose IgG4-related disease (IgG4-RD). IgG subclasses were gathered from laboratory from December 2011 to December 2020. The IgG4-RD positive and negative patients were diagnosed according to the ACR/EULAR classification criteria by extensive chart review. Both methods' results were compared in terms of test characteristics. For BSIN, there were 43 IgG4-RD positive cases and 174 disease negative cases, while for LC-MS/MS, there were 102 IgG4-RD positive cases and 562 disease negative cases. The majority of IgG4-RD patients by BSIN and LC-MS/MS had an elevated IgG4 level, 81% and 86%, respectively. For BSIN, the ROC curve, cut-off value of 1.25 g/L, had a sensitivity of 81% and a specificity of 84%. For LC-MS/MS, the ROC curve, cut-off value of 1.25 g/L, had a sensitivity of 86% and a specificity of 84%. The responder index score to IgG4 level r-correlation value for BSIN and LC-MS/MS was 0.5 and 0.6, respectively. In our center, LC-MS/MS and BSIN are equivalent test methods in IgG4-RD diagnosis. IgG4 level does correlate with disease activity by the responder index. LC-MS/MS is a valid and equally reliable alternative to BSIN in the diagnosis of IgG4-related disease.
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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 | 0.010 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".