Incidence of brain metastasis at initial presentation of lung cancer
Bibliographic record
Abstract
BACKGROUND: No reliable estimates are available on the incidence of brain metastasis (BM) in cancer patients. This information is valuable for planning patient care and developing measures that may prevent or decrease the likelihood of metastatic brain disease. METHODS: We report the first population-based analysis on BM incidence at cancer diagnosis using the Kentucky Cancer Registry (KCR) and Alberta Cancer Registry (ACR). All cancer cases with BM were identified from KCR and ACR, with subsequent focus on metastases from lung primaries; the annual number of BMs at initial presentation was derived. Comparisons were made between Kentucky and Alberta for the stage and site of organ involvement of lung cancer. RESULTS: Low incidence of BM was observed in the United States until mandatory reporting began in 2010. Both the KCR and ACR recorded the highest incidence of BM from lung cancer, with total BM cases at initial presentation occurring at 88% and 77%, respectively. For lung cancer, stage IV was the most common stage at presentation for both registries and ranged from 45.9% to 57.2%. When BM from lung was identified, the most common synchronous organ site of metastasis was osseous, occurring at 28.4%. CONCLUSION: Our analysis from the Kentucky and Alberta cancer registries similarly demonstrated the aggressive nature of lung cancer and its propensity for BM at initial presentation. Besides widespread organ involvement, no synchronous organ site predicted BM in lung cancer. BM is a common and important clinical outcome, and use of registry data is becoming more available.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".