P.094 Incidence of pathologically confirmed primary malignant brain tumours in Newfoundland and Labrador: an eight-year review spanning 2015-2022
Bibliographic record
Abstract
Background: Considering regional and temporal trends, we sought to explore the incidence of primary malignant brain tumours in Newfoundland and Labrador. Methods: We reviewed all primary, malignant brain tumour cases from 2015-2022 confirmed by St. John’s Health Sciences Centre pathology reports. Incidence rates were standardized using the 2011 Canadian standard population. Results: We included 362 cases. The average annual age-standardized incidence rate of primary, malignant brain tumours per 100,000 was 7.0 (95% CI: 6.3-7.7), lower than the national average (7.93; 95% CI: 7.78-8.08). The incidence of glioblastoma (5.1; 95% CI: 4.5-5.7) was significantly higher than the national average (4.05; 95% CI: 3.95-4.16). Temporal trends revealed that oligodendroglioma incidence spiked from 0.5 (95% CI: 0.2-0.7) in 2015-2019 to 1.5 (95% CI: 0.4-2.6) in 2020 before returning to baseline in 2022. Regional trends indicated a lower incidence of malignant tumours in Labrador-Grenfell (5.1; 95% CI: 2.5-7.6), compared to 6.9 (95% CI: 6.2-7.6) averaged elsewhere. Conclusions: Higher rates of glioblastoma in Newfoundland and Labrador could have a genetic or multi-factorial cause. The increased occurrence of oligodendroglioma during the COVID-19 pandemic necessitates broader investigation, potentially linked to delays in patient care during this period. Regional trends could suggest less access to care in rural populations and underestimated incidence.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".