The Positive Predictive Value of a Very High Serum IgG4 Concentration for the Diagnosis of IgG4-Related Disease
Bibliographic record
Abstract
OBJECTIVE: Serum IgG4 concentrations are used to evaluate a diagnosis of IgG4-related disease (IgG4-RD), but the positive predictive value (PPV) of a very high IgG4 level is uncertain. This study evaluated the PPV of a very high IgG4 concentration for diagnosing IgG4-RD. METHODS: The data warehouses of 2 large academic healthcare systems were queried for IgG4 concentration test results. Cases with serum IgG4 concentrations > 5× the upper limit of normal (ULN) were included. Cases of IgG4-RD were determined using the American College of Rheumatology/European Alliance of Associations for Rheumatology (ACR/EULAR) classification criteria. The PPV for IgG4-RD of an IgG4 concentration > 5× ULN was estimated. Other conditions associated with very high IgG4 concentrations and specific features of IgG4-RD cases were characterized. RESULTS: IgG4 concentrations were available in 32,206 cases. Of these, 3039 (9.4%) had elevated IgG4 concentrations, and a final cohort of 191 (0.6%) cases had IgG4 concentrations > 5× ULN (median age 66 yrs, 72% male). The PPV of an IgG4 concentration > 5× ULN for a diagnosis of IgG4-RD was 75.4% (95% CI 68.7-81.3). In the remaining cases, elevated IgG4 concentrations were observed among patients with malignancies, autoimmune diseases, and infections. CONCLUSION: The majority of cases with serum IgG4 concentrations > 5× ULN in this study had IgG4-RD. These data support the high weight placed on very high serum IgG4 concentrations in the ACR/EULAR classification criteria. However, 25% of cases with very high IgG4 concentrations had an alternative diagnosis, underscoring the importance of considering the broad differential of etiologies associated with an elevated IgG4 concentration when evaluating a patient.
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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 | 0.035 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".