The clinical outcomes and healthcare resource utilization in IgG4-related disease: a claims-based analysis of commercially insured adults in the United States
Bibliographic record
Abstract
OBJECTIVES: IgG4-related disease (IgG4-RD) can affect nearly any organ and is often treated with glucocorticoids, which contribute to organ damage and toxicity. Comorbidities and healthcare utilization in IgG4-RD are poorly understood. METHODS: We conducted a cohort study using claims data from a US managed care organization. Incident IgG4-RD cases were identified using a validated algorithm; general population comparators were matched by age, sex, race/ethnicity and index date. The frequency of 21 expert-defined clinical outcomes associated with IgG4-RD or its treatment and healthcare-associated visits and costs were assessed 12 months before and 36 months after the index date (date of earliest IgG4-RD-related claim). RESULTS: There were 524 cases and 5240 comparators. Most cases received glucocorticoids prior to (64.0%) and after (85.1%) the index date. Nearly all outcomes, many being common glucocorticoid toxicities, occurred more frequently in cases vs comparators. During follow-up, the largest differences between cases and comparators were seen for gastroesophageal reflux disease (prevalence difference: +31.2%, P < 0.001), infections (+17.3%, P < 0.001), hypertension (+15.5%, P < 0.01) and diabetes mellitus (+15.0%, P < 0.001). The difference in malignancy increased during follow-up from +8.8% to +12.5% (P < 0.001). Some 17.4% of cases used pancreatic enzyme replacement therapy during follow-up. Over follow-up, cases were more often hospitalized (57.3% vs 17.2%, P < 0.01) and/or had an emergency room visit (72.0% vs 36.7%, P < 0.01); all costs were greater in cases than comparators. CONCLUSIONS: Patients with IgG4-RD are disproportionately affected by adverse outcomes, some of which may be preventable or modifiable with vigilant clinician monitoring. Glucocorticoid-sparing treatments may improve these outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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 | 0.000 |
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 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".