Brain Metastases among Cancer Patients Diagnosed from 2010–2017 in Canada: Incidence Proportion at Diagnosis and Estimated Lifetime Incidence
Bibliographic record
Abstract
The incidence of BM among Canadian cancer patients is unknown. We aimed to estimate IP of BM at the time of cancer diagnosis and during the lifetime of patients with selected primary cancers. Data on BM at diagnosis from 2010-2017 was obtained from the CCR. Site-specific IPs of BM were estimated from provincial registries containing ≥90% complete data on BM. The CCR IP estimates and the IP estimates from literature were applied to the total diagnosed primary cancers to estimate the number of concurrent BM and lifetime BM from 2010-2017 in Canada, respectively. The annual average number of patients with BM at diagnosis from all cancer sites was approximately 3227. The site-specific IPs of BM at diagnosis were: lung (9.42%; 95% CI: 9.16-9.68%), esophageal (1.58%; 95% CI: 1.15-2.02%), kidney/renal pelvis (1.33%; 95% CI: 1.12-1.54%), skin melanoma (0.73%; 95% CI: 0.61-0.84%), colorectal (0.22%; 95% CI: 0.18-0.26%), and breast (0.21%; 95% CI: 0.17-0.24%). Approximately 76,546 lifetime BM cases (or 5.70% of selected fifteen primary cancers sites) were estimated to have occurred from the 2010-2017 cancer patient cohort. These findings reflect results of population analyses in the US and Denmark. We recommend improved standardization of the collection of BM data within the CCR.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| 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".