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Record W4405316354 · doi:10.1016/j.lpm.2024.104266

Treatment of elderly and frail myeloma patients

2024· review· es· W4405316354 on OpenAlexaff
Steven Chun-Min Shih, Alissa Visram, Hira Mian

Bibliographic record

VenueLa Presse Médicale · 2024
Typereview
Languagees
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsMcMaster UniversityOttawa HospitalPrincess Margaret Cancer Centre
Fundersnot available
KeywordsMedicineChimeric antigen receptorDiseaseImmunotherapyPopulationMultiple myelomaIntensive care medicineCancerGerontologyInternal medicine

Abstract

fetched live from OpenAlex

Multiple myeloma (MM) is an incurable cancer of older adults. Given the aging population, the prevalence of older adults with MM is expected to further increase over the next decade. Challenges in treating older adults result from the heterogeneity of both aging itself and the disease. Over the past two decades, tremendous progress has been made in improving the outcome in this age group with novel therapeutics, including immunomodulatory drugs, proteasome inhibitors, and more recently anti-CD38 monoclonal antibodies, becoming an integral part of initial treatment. Further improvements are expected over the next decade with novel immunotherapy, including T-cell engagers and chimeric antigen receptor therapies. With additional novel treatments, assessment of patient frailty will become increasingly important in balancing the optimal treatment of patients. In this review, we focus on the treatment of elderly and frail older adults with MM. The first part of our review will focus on pertinent investigations, considerations for treatment initiation and initial risk stratification, including frailty assessment prior to treatment initiation. In the second part, we will focus on the overall goals of treatment and therapeutic options for newly diagnosed and those with relapsed/refractory MM, including novel immunotherapy and supportive care. Lastly, we will end this review by highlighting current knowledge gaps and providing suggestions for future directions to further improve outcomes among older adults with MM.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.962
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.039
GPT teacher head0.352
Teacher spread0.313 · 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 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

Citations4
Published2024
Admission routes1
Has abstractyes

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