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Record W2017001365 · doi:10.1186/1471-2377-11-9

SMARCB1/INI1 germline mutations contribute to 10% of sporadic schwannomatosis

2011· article· en· W2017001365 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBMC Neurology · 2011
Typearticle
Languageen
FieldMedicine
TopicNeurofibromatosis and Schwannoma Cases
Canadian institutionsMontreal Children's Hospital
FundersNational Cancer InstituteInstitut National Du Cancer
KeywordsSMARCB1GermlineMedicineGermline mutationNeurosurgeryNeurologyMutationGeneticsBiologyGeneTranscription factorRadiologyPsychiatry

Abstract

fetched live from OpenAlex

BACKGROUND: Schwannomatosis is a disease characterized by multiple non-vestibular schwannomas. Although biallelic NF2 mutations are found in schwannomas, no germ line event is detected in schwannomatosis patients. In contrast, germline mutations of the SMARCB1 (INI1) tumor suppressor gene were described in familial and sporadic schwannomatosis patients. METHODS: To delineate the SMARCB1 gene contribution, the nine coding exons were sequenced in a series of 56 patients affected with a variable number of non-vestibular schwannomas. RESULTS: Nine variants scattered along the sequence of SMARCB1 were identified. Five of them were classified as deleterious. All five patients carrying a SMARCB1 mutation had more multiple schwannomas, corresponding to 10.2% of patients with schwannomatosis. They were also diagnosed before 35 years of age. CONCLUSIONS: These results suggest that patients with schwannomas have a significant probability of carrying a SMARCB1 mutation. Combined with data available from other studies, they confirm the clinical indications for genetic screening of the SMARCB1 gene.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.051
GPT teacher head0.276
Teacher spread0.225 · 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