Distribution of metastatic sites in patients with prostate cancer: A population‐based analysis
Bibliographic record
Abstract
BACKGROUND: There is few data on what constitutes the distribution of metastatic sites in prostate cancer (PCa). The aim of our study was to systematically describe the most common sites of metastases in a contemporary cohort of PCa patients. METHODS: Patients with metastatic PCa were abstracted from the Nationwide Inpatient Sample (1998-2010). Most common metastatic sites within the entire population were described. Stratification was performed according to the presence of single or multiple (≥ 2 sites) metastases. Additionally, we evaluated the distribution of metastatic sites amongst patients with and without bone metastases. RESULTS: Overall, 74,826 patients with metastatic PCa were identified. The most common metastatic sites were bone (84%), distant lymph nodes (10.6%), liver (10.2%), and thorax (9.1%). Overall, 18.4% of patients had multiple metastatic sites involved. When stratifying patients according to the site of metastases, only 19.4% of men with bone metastases had multiple sites involved. Conversely, among patients with lymph nodes, liver, thorax, brain, digestive system, retroperitoneum, and kidney and adrenal gland metastases the proportion of men with multiple sites involved was 43.4%, 76.0%, 76.7%, 73.0%, 52.2%, 60.9%, and 76.4%, respectively. When focusing exclusively on patients with bone metastases, the most common sites of secondary metastases were liver (39.1%), thorax (35.2%), distant lymph nodes (24.6%), and brain (12.4%). CONCLUSIONS: Although the majority of patients with metastatic PCa experience bone location, the proportion of patients with atypical metastases is not negligible. These findings might be helpful when planning diagnostic imaging procedures in patients with advanced PCa.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".