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Record W3091817477 · doi:10.1007/s12325-020-01520-w

Mesothelin Expression in Patients with High-Grade Serous Ovarian Cancer Does Not Predict Clinical Outcome But Correlates with CD11c+ Expression in Tumor

2020· article· en· W3091817477 on OpenAlexaff
Isabelle Magalhaes, Josefin Fernebro, Sulaf Abd Own, Daria Glaessgen, Sara Corvigno, Mats Remberger, Jonas Mattsson, Hanna Dahlstrand

Bibliographic record

VenueAdvances in Therapy · 2020
Typearticle
Languageen
FieldMedicine
TopicOccupational and environmental lung diseases
Canadian institutionsPrincess Margaret Cancer CentreUniversity Health Network
FundersVetenskapsrådetCancerfonden
KeywordsMesothelinOvarian cancerMedicineOncologySerous fluidImmunohistochemistryCancer researchInternal medicineCancerPathology

Abstract

fetched live from OpenAlex

INTRODUCTION: Mesothelin (MSLN) is overexpressed in several tumors including ovarian cancer and is the target of current trials. There is limited and conflicting data on MSLN prognostic impact in ovarian cancer. METHODS: We performed a retrospective study on patients with high-grade serous ovarian cancer, analyzing MSLN expression by immunohistochemistry and examining the correlation of its expression to overall and progression-free survival. Correlations of expression of MSLN, CD8, and macrophage markers in different tumor compartments were also investigated. RESULTS: Positive MSLN expression was detected in 55.1% of primary tumors and 51.5% of the metastases. MSLN expression was not correlated with survival. We observed a significant positive correlation (r = 0.34, p = 0.01) between MSLN expression in the metastatic site and CD11c expression in total tumor area and perivascular area in the primary tumor. CONCLUSION: cells on immunotherapy outcome should be further explored.

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 score0.005
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 head0.016
GPT teacher head0.303
Teacher spread0.287 · 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

Citations18
Published2020
Admission routes1
Has abstractyes

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