Resolution of Spurious Immunonephelometric IgG Subclass Measurement Discrepancies by LC-MS/MS
Bibliographic record
Abstract
BACKGROUND: The Binding Site immunonephelometric (IN) IgG subclass reagents (IgG1, IgG2, IgG3, IgG, BSIN) are used for assessment of both immunodeficiency and IgG4-related disease (IgG4-RD). In our laboratory, suspected analytic errors were noted in patients with increases in IgG4: The sum of the individual IgG subclasses was substantially greater than the measured total IgG concentrations (unlike samples with normal IgG4), and the IgG4 concentration was always less than the IgG2 concentration. METHODS: We developed a tryptic digest LC-MS/MS method to quantify IgG1, IgG2, IgG3, and IgG4 in serum. Samples with IgG4 concentrations ranging from <0.03 g/L to 32 g/L were reanalyzed by LC-MS/MS, and a subset was also reanalyzed by Siemens IN (SIN) subclass measurements. RESULTS: Multivariate linear regression identified 3 subclass tests with multiple predictors of the measured subclass concentration. For these 3 subclasses, the predominant predictors were (in terms of LC-MS/MS IgG subclass measurement coefficients) BSIN IgG1 = 0.89·IgG1 + 0.4·IgG4; BSIN IgG2 = 0.94·IgG4 + 0.89·IgG2; and SIN IgG2 = 0.72·IgG2 + 0.24·IgG4. CONCLUSIONS: There is apparent IgG4 cross-reactivity with select IN subclass measurements affecting tests from both vendors tested. These findings can be explained either by direct cross-reactivity of the IN reagents with the IgG4 subclass or unique physicochemical properties of IgG4 that permit nonspecific binding of IgG4 heavy chain to other IgG immunoglobulin heavy chains. Irrespective of the mechanism, the observed intermethod discrepancies support the use of LC-MS/MS as the preferred method for measurement of IgG subclasses when testing patients with suspected IgG4-RD.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".