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An Update of the Management of Multiple Myeloma: The Changing Landscape

2005· article· en· W2102630035 on OpenAlexaff
Donna Reece

Bibliographic record

VenueHematology · 2005
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMultiple myelomaMedicineBortezomibClinical trialDiseaseMalignancyTransplantationBioinformaticsOncologyImmunologyInternal medicineBiology

Abstract

fetched live from OpenAlex

The management of multiple myeloma is rapidly changing. Cytogenetic, molecular and proteomic techniques have led to a better understanding of the pathophysiology of this heterogeneous malignancy. Novel agents designed to interrupt myeloma growth and survival pathways have entered into clinical usage with unprecedented speed, while new prognostic systems based on clinical and biologic features, such as cytogenetic abnormalities, have been developed. A plethora of clinical trials have been initiated utilizing novel agents, alone or in conjunction with established modalities such as conventional cytotoxic agents and stem cell transplantation. These newer treatments have increased the antitumor response rates in this disease and have provided options for patients whose disease has become resistant to conventional therapy. A major challenge is to define the optimal use of these new agents and combinations in order to significantly impact the natural history of myeloma.

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 score0.191
Threshold uncertainty score0.157

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 head0.018
GPT teacher head0.301
Teacher spread0.283 · 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
Published2005
Admission routes1
Has abstractyes

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