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Record W2518797132 · doi:10.4103/0971-5851.190359

Efficacy and safety of eribulin mesylate in advanced soft tissue sarcomas

2016· review· en· W2518797132 on OpenAlexaff
Jonathan Noujaim, Salma Alam, Khin Thway, Robin L. Jones

Bibliographic record

VenueIndian Journal of Medical and Paediatric Oncology · 2016
Typereview
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsHôpital Maisonneuve-Rosemont
Fundersnot available
KeywordsEribulinMedicineSoft tissue sarcomaDacarbazineInternal medicineOncologyTrabectedinSarcomaMetastatic breast cancerLiposarcomaAnthracyclineChemotherapyBreast cancerCancerPathology

Abstract

fetched live from OpenAlex

Despite recent advances in the field, treatment options for metastatic soft tissue sarcoma patients are limited. Eribulin, an antimitotic derived from the natural marine sponge product halichondrin B, is currently approved for the treatment of metastatic breast cancer. Following the promising activity of eribulin in sarcoma in a Phase II trial, the drug was recently compared to dacarbazine in pretreated advanced leiomyosarcoma (LMS) and liposarcoma (LPS) patients in a Phase III trial. Eribulin was associated with a significant 2-month improvement in median overall survival compared to dacarbazine (13.5 vs. 11.5 months, heart rate: 0.768) despite no documented significant difference in progression-free survival. In a subgroup analysis, the survival advantage associated with eribulin was evident in the LPS subgroup but not in the LMS subgroup. Following these encouraging results, the Food and Drug Administration has approved eribulin for the treatment of advanced LPS for patients who received prior anthracycline chemotherapy. In this short review, we will evaluate the evidence for eribulin in soft tissue sarcoma, highlight its mechanisms of action, and summarize the results of the major preclinical and clinical studies with a particular focus on the results of the Phase III trial.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.984
Threshold uncertainty score0.762

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.367
Teacher spread0.344 · 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 designOther design
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

Citations3
Published2016
Admission routes1
Has abstractyes

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