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Record W2117718610 · doi:10.5430/jst.v5n1p44

Immunohistochemical study of BRAF V600E mutant protein expression in high-grade sarcomas

2015· article· en· W2117718610 on OpenAlexvenueno aff
Alfredo L. Valente, Kerry Whiting, Jamie Tull, Charlene Maciak, Shengle Zhang

Bibliographic record

VenueJournal of Solid Tumors · 2015
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsImmunohistochemistryVemurafenibV600EMedicinePathologyMelanomaCancer researchMutationBiologyMetastatic melanoma

Abstract

fetched live from OpenAlex

BRAF V600E is a mutation present in numerous neoplasms, including melanomas, thyroid, colorectal and ovarian carcinomas, gastrointestinal stromal tumors, and Langerhans’ cell histiocytosis. Vemurafenib, a BRAF V600E kinase inhibitor has been successfully used in the treatment of melanoma. The role of this mutation in unclassified high-grade sarcomas, malignancies with very limited treatment options, has not been widely studied. Because of the availability of a highly sensitive and specific antibody (VE1) against the BRAF V600E mutant protein, we tested 48 cases of unclassified high-grade sarcomas. Cytoplasmic expression intensity was graded as negative (0), or positive (2+ or 3+) by two pathologists and a pathology resident. Forty one out of 48 specimens remained intact in the cores after immunohistochemistry (IHC) processing. Six of the 41 cases (15%) were scored as positive. In addition, non-specific nuclear staining was detected in 12/88 cores (14%). The 6 positive cases were tested for the BRAF V600E mutation by RT-PCR, and all were negative. Based on these results, we concluded that BRAF V600E mutation is rare in unclassified high-grade sarcomas, and because of the non-specific staining, results should be interpreted with caution.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.439

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)0.0000.000

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 head0.031
GPT teacher head0.312
Teacher spread0.281 · 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

Citations2
Published2015
Admission routes1
Has abstractyes

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