Method Limitations in LC-MS/MS and Immunonephelometric Measurement of IgG Subclasses
Bibliographic record
Abstract
Methodologic limitations in IgG subclass measurements have been previously described: Immunonephelometric IgG subclass measurements may produce IgG4 measurement errors due to the hook effect (1), and IgG1 and IgG2 measurement errors due to cross reactivity of these reagents with the IgG4 subclass immunoglobulins (2). We highlight 2 new sources of methodologic error that can affect IgG subclass measurements: 1) a single nucleotide polymorphism (SNP)-associated amino acid polymorph induced error in a reported LC-MS/MS method for the measurement of the IgG4 subclass (2), and 2) the hook effect affecting the immunonephelometric measurement of the IgG3 subclass. Review of the Ensembl 2018 database (3) revealed a SNP with minor allele frequency of approximately 7% in the African American population that would change the mass of the precursor ion of the signature IgG4 peptide (TTPPVLDSDGSFFLYSR vs TTPPVLDSDGSFFLYSK) from the previously described IgG4 method (2). Theoretically, carriers of this peptide would have apparent IgG4 measurements approximately 50% of the true value when tested with the LC-MS/MS IgG4 method.
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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 | 0.080 | 0.076 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".