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Record W4297237848 · doi:10.1002/cam4.5245

Real‐world data on lenalidomide dosing and outcomes in patients newly diagnosed with multiple myeloma: Results from the Canadian Myeloma Research Group Database

2022· article· en· W4297237848 on OpenAlexaffabout
Hira Mian, Richard LeBlanc, Martha Louzada, Esther Masih‐Khan, Arleigh McCurdy, Christopher P. Venner, Julie Stakiw, Moustafa Kardjadj, Victor H. Jimenez‐Zepeda, Michaël Sébag, Darrell White, Muhammad Aslam, Kevin Song, Tony Reiman, Rami Kotb, Engin Gul, Donna Reece

Bibliographic record

VenueCancer Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsSaint John Regional HospitalBC Cancer AgencyQueen Elizabeth II Health Sciences CentreDalhousie UniversityMcGill UniversityVancouver General HospitalPrincess Margaret Cancer CentreUniversity of CalgaryCanadian Apheresis GroupCancerCare ManitobaOttawa HospitalOttawa Regional Cancer FoundationUniversité de MontréalUniversity of SaskatchewanUniversity of AlbertaHôpital Maisonneuve-RosemontMcMaster University
FundersCelgene
KeywordsLenalidomideMultiple myelomaMedicineDosingDexamethasoneInternal medicineOncology

Abstract

fetched live from OpenAlex

Using the Canadian Myeloma Research Group Database, a retrospective study of 167 newly diagnosed, transplant-ineligible patients with multiple myeloma (MM) that received lenalidomide-dexamethasone as front-line treatment was conducted to understand the impact of lenalidomide dosing. Starting dose modifications were common, 42% of patients started on lenalidomide <25 mg with normal renal function. During treatment course, 35% of patients required further dose reduction. Dose reductions in the first year did not have an impact on progression free survival or overall survival. Further studies need to be conducted to understand the impact of dosing strategies of anti-MM agents in the real world.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.753

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.108
GPT teacher head0.383
Teacher spread0.275 · 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 designObservational
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

Citations6
Published2022
Admission routes2
Has abstractyes

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