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Record W4416123462 · doi:10.3390/curroncol32110633

Von Hippel–Lindau Disease-Associated Endolymphatic Sac Tumours: Seven Cases and Genotype–Phenotype Features

2025· article· en· W4416123462 on OpenAlexvenueno aff
Qin Wang, Junhui Huang, Zhenqiang Zhao, Yu Su, Nan Wu, S. Yang, Weidong Shen, Na Sai, Weiju Han

Bibliographic record

VenueCurrent Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicEar and Head Tumors
Canadian institutionsnot available
Fundersnot available
KeywordsPathologicalMissense mutationFacial nerveHearing lossEndolymphatic sacExonGenetic testingAnastomosis

Abstract

fetched live from OpenAlex

Von Hippel–Lindau disease-associated endolymphatic sac tumors (VHL-associated ELSTs) present diagnostic challenges due to their rarity and nonspecific symptoms. This study describes clinical, pathological and genotypic features to guide treatment. We retrospectively analyzed seven patients with VHL-associated ELSTs. The mean age of otologic symptom [hearing loss (100%) and facial nerve paralysis (85.71%)] onset was 22.43 ± 8.68 years (range: 10–33). Surgical management included trans-labyrinthine and subtotal temporal bone resection approaches. Among three patients with severe preoperative facial nerve dysfunction, two underwent great auricular nerve grafting improved to House–Brackmann grade IV, while one receiving hypoglossal–facial nerve anastomosis reached grade V. Genetic testing identified pathogenic VHL gene missense mutations in three patients. Two female patients demonstrated disease progression during pregnancy. Literature analysis revealed exon-specific patterns: Exon 1 mutations correlated with cerebellar/spinal hemangioblastomas in female patients, while Exon 3 mutations were associated with multisystem tumors. These findings support that VHL-associated ELSTs manifest early with otologic symptoms and demonstrate exon-specific phenotypic patterns. Optimal management requires complete surgical resection, genetic diagnosis, and a multidisciplinary approach to address these complex tumors and achieve favorable outcomes.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.703

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.054
GPT teacher head0.388
Teacher spread0.334 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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