Trajectory of Healthcare Resource Utilization in Giant Cell Arteritis: A Population-based Study
Bibliographic record
Abstract
OBJECTIVE: To estimate the healthcare resource utilization (HRU) in patients with giant cell arteritis (GCA) compared with the general population in southern Sweden. METHODS: The study sample comprised 653 patients with GCA along with 10 age-, sex-, and residency area-matched reference subjects per patient. Data on public and private healthcare consultations and hospitalizations were extracted from the Skåne Healthcare Register. We assessed trajectories of primary and specialist healthcare visits, as well as hospital admissions and inpatient days from 3 years before through 5 years after the date of GCA diagnosis for patients and matched references. HRU was analyzed using generalized estimating equations adjusted for sex, age at the index year, calendar year of diagnosis, education, income, marital status, place of birth, and Charlson Comorbidity Index. Inverse probability weighting was used to account for dropout during study. RESULTS: Patients with GCA had higher rates of healthcare visits than the references from the year before GCA diagnosis and up to 4 years after diagnosis, with the largest relative (rate ratio 1.85, 95% CI 1.68-2.05) and absolute (mean difference 10.2, 95% CI 8.1-12.3 visits per person) differences in the year of diagnosis. Similar trajectories were observed for primary and specialist healthcare visits. For hospital admissions and inpatient days, the differences disappeared 1 year after diagnosis date. CONCLUSION: Patients with GCA utilized healthcare services at a significantly higher rate than the reference population. The increased utilization among Swedish patients with GCA was evident 1 year before and prolonged up to 4 years after diagnosis date.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".