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Record W1970521173 · doi:10.1182/blood-2014-05-578732

The Tao of myeloma

2014· review· en· W1970521173 on OpenAlexaff
Lawrence Boise, Jonathan L. Kaufman, Nizar J. Bahlis, Sagar Lonial, Kelvin P. Lee

Bibliographic record

VenueBlood · 2014
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of Calgary
FundersNational Institute of Allergy and Infectious DiseasesNational Cancer Institute
KeywordsMultiple myelomaPlasma cellProteasomeMalignancyHematologic malignancyCancer researchCarfilzomibProteasome inhibitorDiseaseLenalidomidePlasma cell neoplasmCancerBiologyMedicineComputational biologyImmunologyInternal medicineCell biologyPlasmacytoma

Abstract

fetched live from OpenAlex

Multiple myeloma is a plasma cell malignancy in which significant advances have been observed during the last 15 years. Our understanding of the disease has been advanced through its molecular characterization. We have also seen improvements in patient care with the development of 2 new classes of active agents, proteasome inhibitors and immunomodulatory drugs (IMiDs), resulting in a significant improvement in overall survival of myeloma patients such that it can now be debated as to whether some subsets of myeloma patients can be cured. However, the advances in our understanding of myeloma biology occurred in parallel with advances in treatment as opposed to being directly informed by the research. Moreover, the molecular characterization of malignant plasma cells would not have predicted the effectiveness of these novel therapies.We hypothesize that proteasome inhibitors and IMiDs are highly active because malignant plasma cells are constrained by many of the characteristics of their normal counterparts and these novel therapies target both normal plasma cell biology and the cancer biology of myeloma. Thus, a better understanding of normal plasma cell biology will likely yield as many actionable targets as mapping the genomic landscape of this disease.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.991
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.052
GPT teacher head0.370
Teacher spread0.318 · 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 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

Citations72
Published2014
Admission routes1
Has abstractyes

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