MétaCan
Menu
Back to cohort
Record W4224251450 · doi:10.1016/j.jtho.2022.03.014

Medical and Surgical Care of Patients With Mesothelioma and Their Relatives Carrying Germline BAP1 Mutations

2022· review· en· W4224251450 on OpenAlexaff
Michele Carbone, Harvey I. Pass, Güntülü Ak, H. Richard Alexander, Francine Baumann, Andrew M. Blakely, Raphael Bueno, Aleksandra Bzura, Giuseppe Cardillo, Jane E. Churpek, Irma Dianzani, Assunta De Rienzo, Mitsuru Emi, Salih Emri, Emanuela Felley‐Bosco, Dean A. Fennell, Raja M. Flores, Federica Grosso, Nicholas K. Hayward, Mary Hesdorffer, Chuong D. Hoang, Peter A. Johansson, Hedy L. Kindler, Muaiad Kittaneh, Thomas Krausz, Aaron S. Mansfield, Muzaffer Metintaş, Michael Minaai, Luciano Mutti, Maartje Nielsen, Kenneth J. O’Byrne, Isabelle Opitz, Sandra Pastorino, Francesca Pentimalli, Marc de Perrot, Antonia L. Pritchard, R. Taylor Ripley, B. W. Robinson, Valerie W. Rusch, Emanuela Taioli, Yasutaka Takinishi, Mika Tanji, Anne S. Tsao, Aziz Tuncer, Sebastian Walpole, Andrea Wolf, Haining Yang, Yoshie Yoshikawa, Alicia A. Zolondick, David S. Schrump, Raffit Hassan

Bibliographic record

VenueJournal of Thoracic Oncology · 2022
Typereview
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsToronto General Hospital
FundersARC Training Centre in Lightweight Automotive StructuresJanssen PharmaceuticalsNational Institute of Environmental Health SciencesMedical Research CouncilGenentechAndrew and Jaime Sullivan Family FoundationNational Institutes of HealthHoneywellAldeyra TherapeuticsBeiGeneClovis OncologyAstex PharmaceuticalsBayer FundNational Health and Medical Research CouncilAstraZenecaNovocureMerck Sharp and DohmeUniversity of Hawaiʻi FoundationTakeda Pharmaceuticals U.S.A.AbbVieMedtronicRocheNovartisEMD SeronoMesothelioma Applied Research FoundationMerckGlaxoSmithKlineNational Cancer InstituteBayerMaurice and Joanna Sullivan Family FoundationBristol-Myers SquibbU.S. Department of DefenseEli Lilly and CompanyInventiva PharmaPfizerAriad PharmaceuticalsBoehringer Ingelheim
KeywordsMedicineBAP1GermlineMesotheliomaGermline mutationOncologyInternal medicineIntensive care medicineGeneral surgeryMutationGeneticsPathologyGene

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.026
GPT teacher head0.407
Teacher spread0.381 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations93
Published2022
Admission routes1
Has abstractno

Explore more

Same venueJournal of Thoracic OncologySame topicOccupational and environmental lung diseasesFrench-language works237,207