Comprehensive analysis of cutaneous T‐cell lymphoma (CTCL) incidence and mortality in Canada reveals changing trends and geographic clustering for this malignancy
Bibliographic record
Abstract
BACKGROUND: Previous reports of geographic clustering of cutaneous T-cell lymphoma (CTCL) in Texas, Pittsburgh, and Sweden as well as the occurrence of CTCL in married couples and family members raise a possibility of the existence of an external and potentially preventable trigger(s) for this rare skin cancer. METHODS: The authors studied CTCL incidence and mortality in Canada using 3 distinct population-based cancer databases. Data on patients' sex, age at the time of diagnosis, subtype of CTCL malignancy, reporting province, city, and postal code were analyzed. CTCL cases were mapped across Canada using geographic information systems software. RESULTS: In total, 6685 patients with CTCL were identified in Canada during 1992 through 2010 (CTCL incidence rate, 11.32 cases per million individuals per year), of which 58% were males. The mean age at diagnosis was 59.4 ± 21.5 years. Geographic analysis of patients revealed increased CTCL incidence on the provincial and city levels in several eastern provinces and in Manitoba. An analysis according to postal codes (Forward Sortation Area [FSA]) identified select communities in which several high-incidence FSAs were contiguous or adjacent. Several of these FSAs were located in industrial regions of Canadian cities. Conversely, 3 of 8 low-incidence FSAs were clustered in Ottawa, Ontario, which has very little industrial presence. An analysis of CTCL mortality in Canada corroborated the current incidence findings. CONCLUSIONS: The current results provide a comprehensive analysis of CTCL burden in Canada and highlight several important areas of geographic case clustering. These findings argue that industrial exposures may play an important role in promoting CTCL pathogenesis. Cancer 2017;123:3550-67. © 2017 American Cancer Society.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".