Investigation of the Incidence and Geographic Distribution of Bone and Soft Tissue Sarcomas in Canada: A National Population-Based Study
Bibliographic record
Abstract
Sarcomas are a heterogeneous group of mesenchymal malignancies with various genetic and environmental risk factors. This study analyzed the epidemiology of sarcomas to gain insight into the incidence and mortality rates of these cancers in Canada, as well as to elucidate their potential environmental risk factors. Data for this study were obtained from le Registre Québécois du Cancer (LRQC) and from the Canadian Cancer Registry (CCR) for the period from 1992 to 2010. Mortality data were obtained from the Canadian Vital Statistics (CVS) database for the period from 1992 to 2010 using the International Classification of Diseases for Oncology, ICD-O-3, ICD-9, or ICD-10 codes, for all subtypes of sarcomas. We found that the overall sarcoma incidence in Canada decreased during the study period. However, there were select subtypes with increasing incidence. Peripherally located sarcomas were found to have lower mortality rates compared to axially located sarcomas, as expected. Clustering of Kaposi sarcoma cases in self-identified LGBTQ+ communities and in postal codes with a higher proportion of African-Canadian and Hispanic populations was observed. Forward Sortation Area (FSA) postal codes with a lower socioeconomic status also had higher Kaposi sarcoma incidence rates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".