Distribution and Clustering of Cutaneous T-Cell Lymphoma (CTCL) Cases in Canada During 1992 to 2010
Bibliographic record
Abstract
BACKGROUND: Clustering of patients with cutaneous T-cell lymphoma (CTCL) was reported in several jurisdictions around the world. This rare cancer is known to affect spouses and in some cases multiple members of the same family. These combined results suggest the existence of external disease triggers/promoters. We recently conducted the first comprehensive analysis of CTCL incidence and mortality in Canada, which revealed case clustering in several regions. OBJECTIVES: To extend our previous analysis on CTCL incidence across Canada and to provide all the collected data on CTCL patient incidence in Canada during the period of 1992 to 2010. METHODS: Clinical parameters for patients with CTCL in Canada were analyzed using 2 independent population-based cancer registries: Canadian Cancer Registry and Le Registre Québécois du Cancer. The CTCL incidence rates were examined on different geographical levels, including provinces/territories, cities, and forward sortation areas. RESULTS: Our findings further corroborate our earlier observations of higher CTCL incidence in Newfoundland and Labrador, maritime provinces (Nova Scotia and New Brunswick), and prairie provinces (Manitoba and Saskatchewan). Also, most cities with high CTCL incidence were located in these provinces. Extensive mapping of high-incidence postal codes supports case clustering in a number of communities that are located in the proximity of industrial centres and seaports. CONCLUSIONS: Detailed analysis of CTCL incidence in Canada is critical to fully understand the burden of this disease in our country, to begin the search for a possible external trigger for this lymphoma, and to reform how health care resources are distributed throughout the country to better serve Canadian patients with CTCL.
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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 | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".