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Record W4389676521 · doi:10.1002/hed.27595

<scp>SMARCB1</scp> (<scp>INI1</scp>)‐deficient sinonasal carcinoma manifesting as oral lesions: A report of two cases

2023· article· en· W4389676521 on OpenAlexaff
Jessie Fuoco, Michelle Huang, Najmeh Esfandiari, Christina MacMillan, Aiman Ali, Kanan Dave, Marshall Freilich, Marco Magalhaes

Bibliographic record

VenueHead & Neck · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicChromatin Remodeling and Cancer
Canadian institutionsHumber River Regional HospitalSunnybrook Health Science CentreWestern UniversityHealth Sciences CentreMount Sinai HospitalUniversity of Toronto
Fundersnot available
KeywordsMaxillaMedicineSMARCB1PathologyHead and neckDifferential diagnosisCarcinomaAnatomySurgeryBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Sinonasal carcinomas represent a rare group of malignancies, accounting for less than 5% of all head and neck cancers and a worldwide incidence of less than 1 case per 100 000 inhabitants annually. Despite the restricted anatomical location, sinonasal carcinomas harbor some of the most histologically and molecularly diverse groups of tumors. SMARCB1 (INI1)-deficient sinonasal carcinomas are locally aggressive tumors commonly detected late, leading to devastating morbidity and mortality. CASE REPORT: We present two cases of SMARCB1-deficient sinonasal carcinoma involving the oral cavity and presenting as progressive radiolucent lesions with local swelling associated with maxillary dentition and alveolar bone. Both cases were initially considered odontogenic in origin and involved the destruction of the left anterior maxilla. CONCLUSION: Given the rarity and the variable presentation of these tumors, they pose a challenge for head and neck surgeons, dentists, and pathologists due to the potential overlapping features with odontogenic and non-odontogenic inflammatory and neoplastic lesions. These cases highlight the importance of a multidisciplinary team and include SMARCB1-deficient sinonasal carcinomas in the differential diagnosis of destructive lesions of the maxilla.

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 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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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)0.0000.000

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 head0.042
GPT teacher head0.326
Teacher spread0.284 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations3
Published2023
Admission routes1
Has abstractyes

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