54 ESTIMATING THE INCIDENCE RATE OF POST-DIAGNOSIS BRAIN METASTASES
Bibliographic record
Abstract
Abstract Brain Cancer Canada Travel Award Recipient Brain metastases (BMs) exceed primary CNS tumours and comprise the majority workload in neuro-oncology. However, estimates of post-diagnosis BMs incidence are lacking. We address this gap and provide a population-based 10-year incidence rate of BMs following cancer diagnosis. We identified cancer patients diagnosed between 10/01/2012 and 09/30/2013 in Alberta, who were free from brain tumours at diagnosis. Brain/head-related radiology reports following cancer diagnosis for all patients were retrieved from a provincial radiology database up to 10/31/2022. Stratified by their number of follow-up reports, we drew patient samples from each stratum. All reports from sampled patients were manually labeled for BM (yes/no). We estimated BM incidence with three methods: (1) weighting using sampling weights, (2) simulating BM labels based on BM probability and distributions of the first BM occurrences, and (3) predicting BM labels using a natural language processing model. Of 11,800 eligible cancer patients, a total of 11,826 follow-up reports were identified from 4,647 patients having at least one report. 1,262 reports from 249 sampled patients were labeled. The estimated 10-year incidence rate (per 100,000 person-years) was 437.7, 436.6 (95%CI: 422.7–452.6), and 498.2 (95%CI: 447.8–552.3) from the three methods, respectively. The estimated cumulative incidence of BM at 10-year post-diagnosis was 4.16% (95% CI: 3.74–4.65%, method 2) and 4.43 (95%CI: 3.97–4.90%, method 3). We provide a first estimate of the incidence rate and cumulative incidence of BMs following cancer diagnosis, offering valuable insights for healthcare resource planning.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".