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Record W4206490155 · doi:10.1038/s41408-021-00600-6

Early free light chain reduction following treatment initiation predicts favorable outcome in intact immunoglobulin myeloma

2022· letter· en· W4206490155 on OpenAlexafffund
Jean‐Sébastien Claveau, Sophie Savary Bélanger, Imran Ahmad, Jean‐Sébastien Delisle, Vincent De Guire, Jean Roy, Richard LeBlanc

Bibliographic record

VenueBlood Cancer Journal · 2022
Typeletter
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversité de MontréalHôpital Maisonneuve-Rosemont
FundersUniversité de Montréal
KeywordsMultiple myelomaImmunoglobulin light chainMedicineAntibodyImmunoglobulin AInternal medicineMyeloma proteinImmunologyImmunoglobulin GOncology

Abstract

fetched live from OpenAlex

Multiple myeloma (MM) is characterized by the proliferation of malignant plasma cells [ 1 ]. The neoplastic clones usually secrete an intact immunoglobulin (iIg) or fragments of immunoglobulin (Ig) such as light chains. Intact immunoglobulins are traditionally measured by serum (SPEP) and urine protein electrophoresis, which are currently the gold standards to assess MM [ 2 ]. However, due to long half-lives of iIgs, SPEP is limited in its capacity to detect early changes after initiation of treatment.

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)0.001
metaresearch head score (Gemma)0.006
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 score0.005
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0050.004
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.315
Teacher spread0.279 · 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

Citations7
Published2022
Admission routes2
Has abstractno

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