OTHR-07. ESTIMATING INCIDENCE PROPORTION OF BRAIN METASTASES AT DIAGNOSIS AND LIFETIME INCIDENCE AMONG CANCER PATIENTS DIAGNOSED FROM 2010–2015 IN CANADA
Bibliographic record
Abstract
Abstract INTRODUCTION: The incidence of brain metastases (BM) among Canadian cancer patients is unknown. We aimed to estimate the incidence proportion (IP) of BM at the time of all cancer diagnoses and during follow-up of cancer patients with the top six primary tumours that are most likely to metastasize to the brain. METHODS: Data on BM at diagnosis from 2010–2015 was obtained from the Canadian Cancer Registry (CCR). Site-specific IPs of BM was estimated for patients from provincial registries that achieved ≥90% complete data. These estimates were applied to the total number of newly diagnosed primary cancers to estimate total number of BM at diagnosis from 2010–2015 in Canada. To estimate the number of lifetime BM that arise from six selected primary cancers including lung, breast, skin melanoma, colorectal, kidney/renal pelvis and esophagus, we applied IP estimates reported in the literature. RESULTS: We identified 1,105,905 cancer cases in the CCR from 2010–2015, of which 519,950 (47%) were from the six primaries. The annual average number of patients with BMs at diagnosis from all cancer sites was approximately 2,800 and was highest for lung cancer(2,400).The site-specific IPs of BM at diagnosis were: lung (9.6%;95% CI: 9.3–10.0%), esophageal (2%;95%CI:1.5–2.7%), kidney/renal pelvis (1.3%;95%CI:1.0–1.5%), skin melanoma (1.1%;95%CI:0.9–1.3%), colorectal (0.3%;95%CI:0.2–0.3%), and breast (0.2%;95%CI:0.2–0.3%).Using clinical and population data from the literature, we estimated that nearly 7,400 lifetime BM cases occur annually for these six primaries. CONCLUSIONS: Each year in Canada, approximately 2,800 BMs from all primary cancers are found at the time of diagnosis and approximately 7,400 lifetime BM occur annually from the six selected primary tumours.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".