Increased Mortality for Individuals With Giant Cell Arteritis: A <scp>Population‐Based</scp> Study
Bibliographic record
Abstract
OBJECTIVE: Reports of mortality risks among individuals with giant cell arteritis (GCA) have been mixed. Our aim was to evaluate all-cause mortality among individuals with GCA relative to the general population over time. METHODS: We performed a population-based study in Ontario, Canada using health administrative data. We studied a cohort of 22,677 GCA patients ages ≥50 years that was identified using a validated case definition (with 81% positive predictive value, 100% specificity). General population comparators were residents ages ≥50 years without GCA. Deaths were ascertained from vital statistics. Annual crude, age- and sex-standardized, and age- and sex-specific all-cause mortality rates were determined for individuals with and without GCA between 2000 and 2018. Standardized mortality ratios (SMRs) were estimated. RESULTS: Age- and sex-standardized mortality rates were significantly higher for GCA patients than comparators, and trending to increase over time with 50.0 deaths per 1,000 GCA patients in 2000 (95% confidence interval [95% CI] 34.0-71.1) and 57.6 deaths per 1,000 GCA patients in 2018 (95% CI 50.8-65.2), whereas mortality rates in the general population significantly declined over time. The annual SMRs for GCA patients generally increased over time, with the lowest SMR occurring in 2002 (1.22 [95% CI 1.03-1.40]) and the highest in 2018 (1.92 [95% CI 1.81-2.03]). GCA mortality rates were more elevated for male patients than female patients. CONCLUSION: Over a 19-year period, mortality rates were increased among GCA patients relative to the general population, and more premature deaths were occurring in younger age groups. The relative excess mortality for GCA patients did not improve over time.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".