The Incidence Trends of Primary Brain Tumors in Saskatchewan from 1970 to 2001
Bibliographic record
Abstract
OBJECTIVE: There has been a paucity of information on the epidemiology of primary brain tumors (BTs) in Canada. This study documents epidemiology of primary BTs in Saskatchewan over three decades to define their current state, changing pattern over years and relative distribution in two geographically defined areas of the province. METHODS: Data on all primary BTs from 1970 to 2001 from the Brain Tumor Registry in Saskatchewan was collected. The aggregate data on primary BTs including the time-series for incidence, age, geographic location and sex were statistically analyzed using SPSS 13. Poisson regression was used to model the incidence as a function of decade of diagnosis and age at the time of diagnosis. RESULTS: The average annual incidence of primary BTs was 11.1 per 100,000 person-years (male 12.5 per 100.000 and female 9.8 per 100.000). Males constituted 54.5% of all these tumors. The age distribution of tumors was bimodal with peaks at 5 years and 65 years. During this time, the incidence of primary BTs has increase predominantly in non-malignant types. No difference was found in the rate of all the diagnosed primary BTs combined, meningioma and lymphoma between the northern part (Regina) and southern part (Saskatoon) of the province. CONCLUSIONS: The incidence of BTs in Saskatchewan is more than previously reported in Canada. There is a temporal trend in increasing incidence of some of the BTs predominantly in the non-malignant types. No spatial difference in the incidence of primary BTs was shown in this province. These data will provide useful information to guide the future studies on BTs changing patterns, possible etiologies and efficient resource allocation for management of these diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".