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Record W2954483077 · doi:10.14283/jfa.2019.25

Comparison of Frailty Scores in Newly Diagnosed Patients with Multiple Myeloma: A Review

2019· review· en· W2954483077 on OpenAlexaff
Hira Mian, Melissa Brouwers, C. Tom Kouroukis, Tanya M. Wildes

Bibliographic record

VenueThe Journal of Frailty & Aging · 2019
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of OttawaMcMaster UniversityJuravinski Cancer Centre
Fundersnot available
KeywordsMedicineMultiple myelomaFrailty IndexUsabilityOperationalizationDiseaseInternal medicinePhysical therapyIntensive care medicine

Abstract

fetched live from OpenAlex

Multiple myeloma is a malignant plasma cell disease, which typically affects older patients, with a median age at diagnosis of 70 years. The challenge in treating older patients is to accurately identify 'fit' patients that can tolerate more intensive treatment to maximize disease control, while simultaneously identifying vulnerable or 'frail' patients who may develop toxicity with significant morbidity and mortality, requiring different treatment options or dose modification. Multiple frailty scores have been devised for multiple myeloma over the years in newly-diagnosed patients. This paper gives an overview of the three common frailty measurements: the International Myeloma Working Group Frailty Score, Mayo Clinic Frailty Score and the Revised Myeloma Co-Morbidity Index. We will summarize the derivation, validation, usability and applicability of these scores in different clinical settings, emphasizing the main strengths and limitations for each index score. We will also highlight future directions in the operationalization of frailty in multiple 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)0.001
metaresearch head score (Gemma)0.001
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 score0.800
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.119
GPT teacher head0.415
Teacher spread0.296 · 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

Citations24
Published2019
Admission routes1
Has abstractyes

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