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Record W2049785876 · doi:10.1055/s-0028-1096198

Immunohistochemical Investigation of Hormone Receptors and Vascular Endothelial Growth Factor Concentration in Vestibular Schwannoma

2008· article· en· W2049785876 on OpenAlexaff
Dustin Dalgorf, Corwyn Rowsell, Juan M. Bilbao, Joseph Chen

Bibliographic record

VenueSkull base · 2008
Typearticle
Languageen
FieldMedicine
TopicMeningioma and schwannoma management
Canadian institutionsHealth Sciences CentreUniversity of TorontoSunnybrook Health Science Centre
Fundersnot available
KeywordsImmunohistochemistryMedicineSchwannomaVascular endothelial growth factorVestibular systemEstrogen receptorPathologyReceptorStainingCancerInternal medicineVEGF receptorsBreast cancerRadiology

Abstract

fetched live from OpenAlex

OBJECTIVES: To determine if a relationship exists between the presence of estrogen receptors (ER), progesterone receptors (PR), or vascular endothelial growth factor (VEGF) and the size, growth rate, and behavior of vestibular schwannoma tumors. DESIGN: Nine tumor samples from young female patients with large vestibular schwannoma tumors were preselected because they were presumed to be faster growing, more aggressive tumors. Immunohistochemical staining was performed using monoclonal mouse antibodies to ER, PR, and VEGF. RESULTS: The mean age of the study sample was 32.3 years, mean tumor size was 3.2 cm, and the average growth rate was 0.4 cm per 2 months. The results of immunohistochemical staining for ER and PR in all nine samples were unequivocally negative. Eight of nine tumor samples stained positive for VEGF, with five demonstrating low intensity and three demonstrating moderate intensity staining. CONCLUSIONS: There is histopathological evidence for the expression of VEGF in vestibular schwannomas but not for ER and PR. Further studies are necessary to determine the role of VEGF and other molecular pathways in the growth of vestibular schwannomas and the application of anti-VEGF therapy as a potential treatment option in the future.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.505
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.018
GPT teacher head0.227
Teacher spread0.208 · 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 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

Citations19
Published2008
Admission routes1
Has abstractyes

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