P.092 Incidence of tissue-sampled brain metastases pre- and post-COVID-19 in Newfoundland and Labrador: an eight-year review
Bibliographic record
Abstract
Background: Brain metastases indicate an advanced tumour stage for many cancers. We sought to investigate the incidence change of tissue-sampled brain metastases and its relation to staging challenges during the COVID-19 pandemic in Newfoundland and Labrador. Methods: We reviewed all brain metastasis cases from 2015-2022 requiring first-time tissue sampling according to pathology reports from the St. John’s Health Sciences Centre. Incidence rates were calculated using yearly population data by regional health authorities and standardized using the 2011 Canadian standard population. Results: We included 173 cases. The average annual age-standardized incidence rate of brain metastases requiring tissue sampling per 100,000 increased from 2.5 (95% CI: 2.0-3.1) pre-COVID-19 to 4.1 (95% CI: 3.3-5.0) post-COVID-19. Brain metastases from lung primaries accounted for 69% of this increase. While incidence declined to near-baseline in the Eastern provincial population by 2022 (3.3; 95% CI: 1.5-5.1), incidence rose into 2022 in the Western population (8.6; 95% CI: 3.9-13.2). Conclusions: These data suggest a delayed presentation of malignancies during the COVID-19 pandemic and underscore the importance of prioritized staging during times of strain on healthcare systems. Regional, temporal trends suggest regions distant from tertiary care centres could face challenges in resolving cases with delayed presentation post-COVID-19.
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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 | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".