Toward determining the lifetime occurrence of metastatic brain tumors estimated from 2007 United States cancer incidence data
Bibliographic record
Abstract
Few population estimates of brain metastasis in the United States are available, prompting this study. Our objective was to estimate the expected number of metastatic brain tumors that would subsequently develop among incident cancer cases for 1 diagnosis year in the United States. Incidence proportions for primary cancer sites known to develop brain metastasis were applied to United States cancer incidence data for 2007 that were retrieved from accessible data sets through Centers for Disease Control and Prevention (CDC Wonder) and Surveillance, Epidemiology, and End Results (SEER) Program Web sites. Incidence proportions were identified for cancer sites, reflecting 80% of all cancers. It was conservatively estimated that almost 70 000 new brain metastases would occur over the remaining lifetime of individuals who received a diagnosis in 2007 of primary invasive cancer in the United States. That is, 6% of newly diagnosed cases of cancer during 2007 would be expected to develop brain metastasis as a progression of their original cancer diagnosis; the most frequent sites for metastases being lung and bronchus and breast cancers. The estimated numbers of brain metastasis will be expected to be higher among white individuals, female individuals, and older age groups. Changing patterns in the occurrence of primary cancers, trends in populations at risk, effectiveness of treatments on survival, and access to those treatments will influence the extent of brain tumor metastasis at the population level. These findings provide insight on the patterns of brain tumor metastasis and the future burden of this condition in the United States.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".