Insights into basal cell carcinoma with bone metastasis: a comprehensive review
Bibliographic record
Abstract
Abstract Basal cell carcinoma (BCC), the most common skin cancer worldwide, is closely associated with sunlight exposure and generally exhibits a low metastatic potential, with a frequency ranging from 0.0028% to 0.55%. Despite its rarity, BCC with bony metastases causes important clinical complications. We collected information on published patients with a diagnosis of BCC with bony metastases, and examined patient demographics, tumour characteristics, histological features and treatment modalities to define patterns and outcomes. Our study encompassed 108 patients: 68 men and 40 women with a mean (SD) age of 66.9 (6.4) years. Histologically identified subtypes included 42 nodular, 28 infiltrative, 9 morphoeaform, 5 metatypical and 1 superficial BCC, with 23 patients having a mixed histopathology pattern. The main treatments were surgery (n = 98), chemotherapy (n = 31), immunotherapy (n = 16) and radiotherapy (n = 34). BCC with bone metastases, although rare, requires more attention due to the complexity of management. Histological subtypes such as infiltrative, sclerosing, morphoeaform, basosquamous and micronodular are associated with aggressive behaviour and the detection of symptoms such as bone pain or hypercalcaemia in patients at high risk of metastasis is important for timely diagnosis. Because of the aggressive potential and clinical implications of some subtypes, a personalized management approach with comprehensive histological and molecular profiling is essential to optimize outcomes in patients with BCC with bone metastasis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".