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Record W4385476635 · doi:10.1177/11795549231187264

Mirvetuximab Soravtansine in Platinum-Resistant Ovarian Cancer

2023· review· en· W4385476635 on OpenAlexaff
Eduardo González-Ochoa, Ana Veneziani, Amit M. Oza

Bibliographic record

VenueClinical Medicine Insights Oncology · 2023
Typereview
Languageen
FieldMedicine
TopicOvarian cancer diagnosis and treatment
Canadian institutionsUniversity of TorontoPrincess Margaret Cancer CentreUniversity Health Network
Fundersnot available
KeywordsOvarian cancerMedicineMonoclonal antibodyOncologyCancerChemotherapyCytotoxic T cellInternal medicineDrug resistanceAntibodyImmunologyCancer researchBiologyIn vitro

Abstract

fetched live from OpenAlex

Ovarian cancer is the second leading cause of death from gynecologic malignancies worldwide. Management of platinum-resistant disease is challenging and clinical outcomes with standard chemotherapy are poor. Over the past decades, significant efforts have been made to understand drug resistance and develop strategies to overcome treatment failure. Antibody drug conjugates (ADCs) are a rapidly growing class of oncologic therapeutics, which combine the ability to target tumor-specific antigens with the cytotoxic effects of chemotherapy. Mirvetuximab soravtansine is an ADC comprising an IgG1 monoclonal antibody against the folate receptor alpha (FRα) conjugated to the cytotoxic maytansinoid effector molecule DM4 that has shown promising clinical activity in patients with FR-α-positive ovarian cancer. This review summarizes current evidence of mirvetuximab soravtansine in platinum-resistant ovarian cancer, focusing on clinical activity, toxicity, and future directions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.322
GPT teacher head0.542
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations29
Published2023
Admission routes1
Has abstractyes

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