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Record W2099823824 · doi:10.1002/hed.21277

Expression of matrix metalloproteinase‐1, ‐7, ‐9, ‐13, Ki‐67, and HER‐2 in epithelial‐myoepithelial salivary gland cancer

2009· article· en· W2099823824 on OpenAlexaffabout
Heikki Luukkaa, Pekka Klemi, Ilmo Leivo, Antti Mäkitie, Jonathan C. Irish, Ralph Gilbert, Bayardo Perez‐Ordoñez, Pirkko Hirsimäki, Tero Vahlberg, Atte Kivisaari, Veli‐Matti Kähäri, Reidar Grénman

Bibliographic record

VenueHead & Neck · 2009
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
FundersGenentech
KeywordsMyoepithelial cellImmunohistochemistryMatrix metalloproteinaseSalivary glandOncogeneSalivary gland cancerPathologyCancerMatrix metalloproteinase 9OncologyCancer researchChemistryInternal medicineMedicineCell cycle

Abstract

fetched live from OpenAlex

BACKGROUND: The expression of matrix metalloproteinases (MMPs) in epithelial-myoepithelial salivary gland carcinoma has not been studied previously. METHODS: Immunohistochemistry for MMP-1, -7, -9, -13, Ki-67, and HER-2, as well as HER-2 gene amplification by silver enhanced in situ hybridization was performed in a series of 12 paraffin-embedded histopathologic samples of patients from Canada and Finland. RESULTS: A positive MMP-13 (p = .0022), higher MMP-13 (p = .0274), and higher MMP-9 (p = .0274) index (multiplication of staining intensity by percentage of the positive cells) predicted better overall survival. In disease-specific analysis, higher MMP-9 index (p = .0327) predicted better survival. A higher volume corrected index (VCI) of Ki-67 (p = .0339) predicted worse disease-specific survival. In 1 patient, HER-2 oncogene amplification was observed. CONCLUSION: MMPs and Ki-67 may have prognostic impact in epithelial-myoepithelial carcinoma.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.726

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 head0.018
GPT teacher head0.313
Teacher spread0.295 · 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

Citations22
Published2009
Admission routes2
Has abstractyes

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