Epidemiology of ophthalmic lymphoma in Canada during 1992–2010
Bibliographic record
Abstract
BACKGROUND: Ophthalmic lymphoma (OL) is the most common orbital tumour, particularly in older individuals. Little is known about the epidemiology and geographic distribution of OL in Canada. Descriptive demographic statistics are an important first step in understanding OL burden and are necessary to inform comprehensive national cancer prevention programmes. METHODS: We determined patterns of incidence and geographical distribution of the three major subtypes of OL: extranodal marginal zone B cell lymphoma, follicular lymphoma (FL) and diffuse large B cell lymphoma. Here, we used cases that were diagnosed during 1992-2010 using two independent population-based cancer registries, the Canadian Cancer Registry and Le Registre Québécois du Cancer (LRQC). RESULTS: The OL mean annual age-standardised incidence rate for 1992-2010 was 0.65 cases per million people per year with an average annual increase in the incidence rate of 4.5% per year. The mean age of diagnosis was 65 years. OL incidence rate was the highest in the cities located along the heavily industrialised Strait of Georgia in British Columbia. CONCLUSIONS: Our data on patient age, sex and temporal trends showed similarities with data reported in the USA and Denmark. Additional studies are needed to determine whether the observed increase in OL incidence is genuine or spurious.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 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".