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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".