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Multiple Myeloma: From Front-Line to Relapsed Therapies

2015· review· en· W1507518844 on OpenAlexaff
Philippe Moreau, Cyrille Touzeau

Bibliographic record

VenueAmerican Society of Clinical Oncology Educational Book · 2015
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsHotel Dieu Hospital
Fundersnot available
KeywordsPomalidomideIxazomibLenalidomideThalidomideMedicineCarfilzomibMultiple myelomaBortezomibOncologyInternal medicineProteasome inhibitorRefractory (planetary science)PharmacologyBiology

Abstract

fetched live from OpenAlex

Recent developments in the treatment of multiple myeloma (MM) have led to improvements in response rates and to increased survival. A major advance in the last decade has been the introduction of the novel agents thalidomide, bortezomib, and lenalidomide as part of front-line treatment in both the transplant and nontransplant settings. However, disease relapse is inevitable for the majority of patients and myeloma typically recurs more aggressively with each relapse, eventually leading to the development of treatment-refractory disease. Several phase II and III trials have demonstrated the efficacy of recently approved agents in the setting of relapsed and relapsed and refractory MM, including pomalidomide and carfilzomib. Ixazomib, an oral proteasome inhibitor, and multiple other novel classes of agents are being investigated. These include monoclonal antibodies and histone deacetylase inhibitors, which may further add to the therapeutic armamentarium for this malignancy. Therefore, in a disease characterized by multiple relapses, the optimal sequencing of the different effective options is an important consideration in attempting to prolong survival.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.833
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.217
GPT teacher head0.532
Teacher spread0.315 · 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; both teacher heads agree on what is shown here.

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

Citations48
Published2015
Admission routes1
Has abstractyes

Explore more

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