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Record W4365811103 · doi:10.1007/s44228-023-00036-8

Current Advances in Multiple Myeloma: A Post International Myeloma Society (IMS 2022) Round Table Debate by the International Academy for Clinical Hematology (IACH)

2023· review· en· W4365811103 on OpenAlexaff
Nizar J. Bahlis, Luciano J. Costa, Thierry Façon, Jean‐Luc Harousseau, Salomon Manier, Aurore Perrot, Cyrille Touzeau, Mohamad Mohty

Bibliographic record

VenueClinical Hematology International · 2023
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsHematologyMultiple myelomaMedicineRound tableInternal medicineImmune thrombocytopeniaClinical immunologyOncologyImmunologyComputer science

Abstract

fetched live from OpenAlex

This round table discussion organized by the International Academy for Clinical Hematology (IACH) was dedicated to the 19th annual meeting of the International Myeloma Society (IMS), which was held in Los Angeles between the 25th and 27th August 2022. After some key meetings of the discipline of the field of clinical hematology, the IACH organizes regular round table discussion in order to summarize the flow of information and get the opinion of a panel of experts and the key take-home messages. As part of this discussion, the panellists debated 6 key topics: disease monitoring, management of high-risk multiple myeloma (MM), induction for newly-diagnosed MM, management of relapsed MM, immune reconstitution, and vaccination and cellular therapy in MM.

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)0.005
metaresearch head score (Gemma)0.004
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0110.004

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 head0.221
GPT teacher head0.532
Teacher spread0.311 · 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

Citations3
Published2023
Admission routes1
Has abstractyes

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