Healthcare Use and Direct Cost of Giant Cell Arteritis: A Population-based Study
Bibliographic record
Abstract
OBJECTIVE: To determine the healthcare use and direct medical cost of giant cell arteritis (GCA) in a population-based cohort. METHODS: A well-defined, retrospective population-based cohort of Olmsted County, Minnesota, USA, residents diagnosed with GCA from 1982-2009 was compared to a matched referent cohort from the same population. Standardized cost data (inflation-adjusted to 2014 US dollars) for 1987-2014 and outpatient use data for 1995-2014 were obtained. Use and costs were compared between cohorts through signed-rank paired tests, McNemar's tests, and quantile regression models. RESULTS: Significant annual differences in outpatient costs were observed for patients with GCA in each of the first 4 years (median differences: $2085, $437, $382, $388, respectively). In adjusted analyses, median incremental cost attributed to GCA over a 5-year period was $4662. Compared with matched referent subjects, patients with GCA had higher use of laboratory visit-days annually for each of the first 3 years following incidence/index date, and increased outpatient physician visits for years 0-1, 1-2, and 3-4. Patients with GCA had significantly more radiology visit-days in years 0-1, 3-4, and 4-5, and more ophthalmologic procedures/surgery in years 0-1, 1-2, 2-3, and 4-5 compared to non-GCA. Emergency medicine visits, musculoskeletal, and cardiovascular procedures/surgery were similar between GCA and non-GCA groups throughout the study period. CONCLUSION: Direct medical outpatient costs were increased in the month preceding and in the first 4 years following GCA diagnosis. Higher use of outpatient physician, laboratory, and radiology visits, and ophthalmologic procedures among these patients accounts for the increased cost of care.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".