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Record W2137055964 · doi:10.1097/moh.0b013e32832e3154

Recent trends in the management of newly diagnosed multiple myeloma

2009· review· en· W2137055964 on OpenAlexaff
Donna Reece

Bibliographic record

VenueCurrent Opinion in Hematology · 2009
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMultiple myelomaMedicineMelphalanPrednisoneInternal medicineOncologyAutologous stem-cell transplantationLenalidomideClinical trialTransplantationIntensive care medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Many recent trials have been undertaken that incorporate novel agents into the treatment of newly diagnosed multiple myeloma patients. This review highlights the current status of different approaches to initial therapy in the era of novel drugs. RECENT FINDINGS: The therapy of newly diagnosed patients with multiple myeloma is still usually based on age and eligibility for autologous stem cell transplantation (ASCT). In older patients, several randomized trials have evaluated the addition of a novel agent to oral melphalan and prednisone, while novel agents have been incorporated before, during and after ASCT in younger individuals. Newer investigational approaches that are not age-dependent include continuous myeloma suppression with dexamethasone plus an immunomodulatory derivative, or the use of multiple cycles of combination regimens followed by a treatment break or maintenance therapy. Updated information is now also available regarding the use of nonmyeloablative allogeneic transplantation. The biologic heterogeneity of myeloma is most easily measured in the clinic by fluorescence in-situ hybridization (FISH) cytogenetics, and the detection of adverse cytogenetics is beginning to influence treatment decisions. SUMMARY: Clinical trials have established the superiority of regimens containing novel agents in the initial management of myeloma, although a number of questions remain about the optimal strategy.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.969
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.176
GPT teacher head0.455
Teacher spread0.278 · 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.

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

Citations7
Published2009
Admission routes1
Has abstractyes

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