Regional Variation and Challenges in Estimating the Incidence of Giant Cell Tumor of Bone
Bibliographic record
Abstract
BACKGROUND: Estimating the incidence of giant cell tumor of bone is challenging because few population-based cancer registries record benign bone tumors. We compared two approaches, the indirect (relative index) estimation approach used in The Burden of Musculoskeletal Diseases in the United States (BMUS) and a direct incidence rate approach (from registries that record giant cell tumor), to estimate giant cell tumor incidence in France, Germany, Italy, Spain, the U.K., Sweden, Australia, Canada, Japan, and the U.S. METHODS: Giant cell tumor of bone incidence was calculated with use of the BMUS relative index of giant cell tumor to osteosarcoma in three scenarios (low, base case, and high) from case series. We compared the BMUS approach with the latest data from tumor registries in Australia (1972 to 1996), Japan (2006 to 2008), and Sweden (1993 to 2011) that record giant cell tumors. United Nations population estimates were used to project results to 2013. RESULTS: The low scenario in the BMUS approach reflects data from Unni and Inwards; the incidence of giant cell tumor of bone is 0.34 relative to osteosarcoma. As the incidence of osteosarcoma is 31.4% of the total incidence of bone and joint cancers, the incidence of giant cell tumor is 0.11 times that of all bone and joint cancers. The base scenario reflects the series by Mirra et al., with a giant cell tumor incidence of 0.47 relative to osteosarcoma (0.15 to all bone and joint cancers). The high scenario reflects the series by Ward, with an incidence of 0.84 relative to osteosarcoma (0.26 to all bone and joint cancers). Differences among the three series reflect referral to a national center of excellence compared with referral to a local oncology practice. Registry data indicated a giant cell tumor incidence rate per million per year of 1.33 in Australia, 1.03 in Japan, and 1.11 in Sweden in 2013. The estimated incidence rate per million in the ten countries in 2013 ranged from 1.03 (Japan) to 1.17 (Canada) with use of the registry-based approach and from 0.73 (Japan) for the low scenario) to 2.20 (Germany) for the base case with use of the BMUS approach. CONCLUSIONS: Giant cell tumor of bone affects approximately one person per million per year in the ten countries studied. Estimates derived with use of age-specific incidences from tumor registries were typically within the range of the low and base case BMUS scenarios. We recommend the registry-derived method for estimating the incidence of giant cell tumor.
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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 | 0.042 | 0.099 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".