Projected Worldwide Disease Burden from Giant Cell Arteritis by 2050
Bibliographic record
Abstract
OBJECTIVE: To estimate and project the number of people affected worldwide by giant cell arteritis (GCA) by 2050. Modeling the number of people visually impaired as a result of this disease will help establish the projected morbidity and resource burden. METHODS: A systematic literature review up to December 2013 was conducted using PubMed and ISI Web of Science. Studies reporting an incidence rate for GCA were used to model disease incident cases at regional and national levels. United Nations Population Prospect data were used for population projections. Morbidity burden was established through rates of visual impairment. The associated financial implications were calculated for the United States. RESULTS: The number of incident cases of GCA will increase secondary to an aging population. By 2050, more than 3 million people will have been diagnosed with GCA in Europe, North America, and Oceania. About 500,000 people will be visually impaired. By 2050, in the United States alone, the estimated cost from visual impairment due to GCA will exceed US$76 billion. Inpatient care for patients with active GCA will total about US$1 billion. Management of steroid-related adverse events will increase costs further, with steroid-induced fractures estimated to total US$6 billion by 2050. CONCLUSION: Projecting disease burden for GCA on a global scale allows for optimization of healthcare planning and prioritization of research domains. Additional population-based studies are required to more accurately project worldwide disease burden. Our work highlights the future global disease burden of GCA, and illustrates the associated financial implications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".