Medical and Surgical Care of Patients With Mesothelioma and Their Relatives Carrying Germline BAP1 Mutations
Bibliographic record
Abstract
The most common malignancies that develop in carriers of BAP1 germline mutations include diffuse malignant mesothelioma, uveal and cutaneous melanoma, renal cell carcinoma, and less frequently, breast cancer, several types of skin carcinomas, and other tumor types. Mesotheliomas in these patients are significantly less aggressive, and patients require a multidisciplinary approach that involves genetic counseling, medical genetics, pathology, surgical, medical, and radiation oncology expertise. Some BAP1 carriers have asymptomatic mesothelioma that can be followed by close clinical observation without apparent adverse outcomes: they may survive many years without therapy. Others may grow aggressively but very often respond to therapy. Detecting BAP1 germline mutations has, therefore, substantial medical, social, and economic impact. Close monitoring of these patients and their relatives is expected to result in prolonged life expectancy, improved quality of life, and being cost-effective. The co-authors of this paper are those who have published the vast majority of cases of mesothelioma occurring in patients carrying inactivating germline BAP1 mutations and who have studied the families affected by the BAP1 cancer syndrome for many years. This paper reports our experience. It is intended to be a source of information for all physicians who care for patients carrying germline BAP1 mutations. We discuss the clinical presentation, diagnostic and treatment challenges, and our recommendations of how to best care for these patients and their family members, including the potential economic and psychosocial impact.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".