Prevalence of Lung Metastases among 19,321 Metastatic Colorectal Cancer Patients in Eight Countries of Europe and Asia
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer is one of the most common malignancies in the Western world, and is responsible for about 10% of annual cancer-related deaths. Especially for UICC stage IV, the probability of survival is significantly reduced. Little is known about risk factors for specific metastatic patterns of colorectal cancer that may also influence patients' overall survival. METHODS: We used data from the IQVIA oncology dynamics (OD) database to determine the prevalence of pulmonary metastases in 19,321 patients with UICC stage IV colorectal cancer in eight European and Asian countries. RESULTS: In total, 6132 of 19,321 (31.7%) study patients had lung metastases, with a higher prevalence among patients with rectal (37.5%) than colon (30.1%) cancer. When compared to China as the country with the lowest lung metastases prevalence, the odds for lung metastases were highest in UK (OR: 2.02, 95%CI: 1.80-2.28), followed by Italy (OR: 1.86, 95%CI: 1.52-2.27), Spain (OR: 1.85, 95%CI: 1.64-2.09), and Germany (OR: 1.47, 95%CI: 1.26-1.71). CONCLUSION: The prevalence of pulmonary metastases in UICC stage IV colorectal cancer varies widely among the different analyzed countries. Although the present data are purely descriptive, a possible combination of ethnic, environmental, and health care system-associated differences could be discussed as the underlying cause. Further studies are needed to investigate the reasons for differences in the prevalence of lung metastases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".