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Record W4319073050 · doi:10.1016/j.clml.2023.01.016

First Line Treatment of Newly Diagnosed Transplant Ineligible Multiple Myeloma: Recommendations from the Canadian Myeloma Research Group Consensus Guideline Consortium

2023· review· en· W4319073050 on OpenAlexafffundabout
Julie Anne Côté, Rami Kotb, Debra Bergstrom, Richard LeBlanc, Hira Mian, Ibraheem Othman, Martha Louzada

Bibliographic record

VenueClinical Lymphoma Myeloma & Leukemia · 2023
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsLondon Health Sciences CentreMcMaster UniversityJuravinski Cancer CentreUniversity of SaskatchewanUniversité de MontréalMemorial University of NewfoundlandCancerCare ManitobaCentre hospitalier universitaire de QuébecUniversity of ManitobaWestern UniversityHôpital Maisonneuve-Rosemont
FundersHamilton Health Sciences FoundationDalhousie UniversityHamilton Health SciencesSierra OncologyGlaxoSmithKline
KeywordsMedicineMultiple myelomaGuidelineOncologyInternal medicineFamily medicineIntensive care medicinePathology

Abstract

fetched live from OpenAlex

Although the availability of effective novel treatments has positively impacted the quality of life and survival of newly diagnosed multiple myeloma (MM) patients, benefits in the transplant ineligible MM population may be limited by functional/frailty status. The Canadian Myeloma Research Group Consensus Guideline Consortium proposes consensus recommendations for the first-line treatment of transplant ineligible MM. To address the needs of physicians and people diagnosed with MM, this document further focuses on eligibility for transplant, frailty assessment, management of adverse events, assessment of treatment response, and monitoring for disease relapse. The Canadian Myeloma Research Group Consensus Guideline Consortium will periodically review the recommendations herein and update as necessary.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0030.004
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0040.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.245
GPT teacher head0.457
Teacher spread0.212 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2023
Admission routes3
Has abstractno

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