Nervous system tumors in adult immigrants to Sweden by subsite and histology
Bibliographic record
Abstract
BACKGROUND: The coding of histology of nervous system (NS) tumors with various degrees of malignancies differs between cancer registries, whereby the comparison of incidence rates from one registry to another seems difficult. No study has systematically defined whether the change in the risk of NS tumors upon immigration in adulthood varies by subsite or histology. Therefore, we aimed to address this issue amongst the first-generation immigrants to Sweden based on a large uniform cancer registry data (1958-2006). METHODS: The nationwide Swedish Family-Cancer Database (2008 version; >11.8 million individuals; 1.8 million immigrants; histology code in force since 1958) was used to calculate standardized incidence ratios (SIRs). We analyzed 28,981 adult cases of NS tumors amongst Swedes and 2519 amongst immigrants (age ≥ 30). RESULTS: Significantly decreased risks for brain glioma were amongst German (SIR = 0.64), Eastern European (0.62), some Asian (0.71), Chilean (0.34), and African immigrants (0.52). We found an increased risk for brain meningioma amongst Finns (1.15) and former Yugoslavians (1.33), whilst only Norwegians (0.71) and Latin Americans (0.21) had a decreased risk. The risk for spinal ependymoma and astrocytoma was increased in Germans (3.66) and former Yugoslavians (8.89). We found no significant difference for peripheral nerve tumors between immigrants and the native Swedes. CONCLUSION: Significant differences between risk of NS tumors amongst immigrants and the native Swedes may suggest different risk factor profiles for glioma compared to meningioma indicating a higher etiological role of genetic background or childhood environmental risk factors rather than exposures after immigration.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".