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Record W2597273572 · doi:10.1111/ejh.12876

Economic burden of relapsed or refractory multiple myeloma: Results from an international trial

2017· article· en· W2597273572 on OpenAlexaff
Don Robinson, Robert Z. Orlowski, Michael B. Stokes, Jianming He, Samuel Huse, A.S. Chitnis, Britte Kranenburg, Annette Lam

Bibliographic record

VenueEuropean Journal Of Haematology · 2017
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsEVRAZ (Canada)
Fundersnot available
KeywordsAbsenteeismMedicineIndirect costsQuality of life (healthcare)Multiple myelomaMultivariate analysisClinical trialPhysical therapyRandomized controlled trialFamily medicineInternal medicineNursing

Abstract

fetched live from OpenAlex

OBJECTIVE: The direct cost of relapsed or refractory multiple myeloma (RRMM) is documented; indirect costs are being explored. Healthcare payers seek cost-offsets from therapies that improve clinical outcomes but challenge budgets; employers seek lower absenteeism and better productivity. Study goals were to: (i) identify direct and indirect economic factors of RRMM, and (ii) explore longitudinal relationships between clinical, economic, and health-related quality of life (HRQoL) assessments. METHODS: Economic questionnaire, clinical, and HRQoL data from a multisite, international, randomized, controlled study in RRMM were analyzed. RESULTS: Patients (n=263) were 53.6% male, 91.6% Caucasian; mean age of 62.9 years, median Eastern Cooperative Oncology Group status of 1 (56.3%). Moderate to severe pain or fatigue was reported by 30.4% and 70.6%, respectively. At baseline, ≥1 hospitalization was reported by 107 (41.8%); 182 (71.1%) and 86 (33.6%) reported specialist and family physician visits, respectively. A total of 28 (10.8%) were working: 10 (37.0%) of which reported RRMM-driven absenteeism ≥1 day. Of those who were not working, 110 (48.2%) indicated that it was due to RRMM. Multivariate modeling showed lower hospitalization with a major tumor response (β=-1.44, CI: -2.89 to 0.01, P=.05). CONCLUSIONS: Substantial RRMM indirect, social costs were observed. Better major tumor response may reduce hospital visits.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.412

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
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 science0.0010.000
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 head0.065
GPT teacher head0.358
Teacher spread0.293 · 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 designRandomized trial
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

Citations16
Published2017
Admission routes1
Has abstractyes

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