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Record W2625253716 · doi:10.1038/leu.2017.138

Natural history of relapsed myeloma, refractory to immunomodulatory drugs and proteasome inhibitors: a multicenter IMWG study

2017· article· en· W2625253716 on OpenAlexaff
Shaji Kumar, Meletios Α. Dimopoulos, Efstathios Kastritis, Evangelos Terpos, Hareth Nahi, Hartmut Goldschmidt, Jens Hillengaß, Xavier Leleu, Meral Beksaç, Melissa Alsina, Albert Oriol, Michèle Cavo, Enrique M. Ocio, María‐Victoria Mateos, Elizabeth O’Donnell, Ravi Vij, H M Lokhorst, Niels W.C.J. van de Donk, Chang‐Ki Min, Tomer M. Mark, Ingemar Turesson, Markus Hansson, Heinz Ludwig, Sundar Jagannath, Michel Delforge, Charalampia Kyriakou, Parameswaran Hari, Ulf‐Henrik Mellqvist, Saad Z. Usmani, Dominik Dytfeld, Ashraf Badros, Philippe Moreau, K Kim, J H Lee, Chaim Shustik, Daniel D. Waller, Wee Joo Chng, Shuji Ozaki, Javier de la Rubia, Hyeon‐Seok Eom, Laura Rosiñol, Juan José Lahuerta, Anna Sureda, Jin Seok Kim, B G M Durie

Bibliographic record

VenueLeukemia · 2017
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsMcGill UniversityMcGill University Health Centre
FundersJanssen Pharmaceuticals
KeywordsMultiple myelomaMedicineInternal medicineCarfilzomibOncologyProteasome inhibitorLenalidomideRefractory (planetary science)HematologyNatural historyPomalidomideBortezomibBiology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.294
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 source (direct Gemma or distilled Codex), 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

Citations354
Published2017
Admission routes1
Has abstractno

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