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Record W2970333266 · doi:10.3747/co.26.5421

Emerging Therapies for the Treatment of Relapsed or Refractory Diffuse Large B Cell Lymphoma

2019· review· en· W2970333266 on OpenAlexaffvenueabout
Pamela Skrabek, Sarit Assouline, Anna Christofides, D. Blair Macdonald, Anca Prica, Randeep Sangha, Barbara A. Matthews, Laurie H. Sehn

Bibliographic record

VenueCurrent Oncology · 2019
Typereview
Languageen
FieldMedicine
TopicLymphoma Diagnosis and Treatment
Canadian institutionsUniversity of British ColumbiaBC Cancer AgencyRoche (Canada)University of TorontoUniversity of AlbertaFluidigm (Canada)ImpactMcGill UniversityPrincess Margaret Cancer CentreCancerCare ManitobaOttawa HospitalUniversity of ManitobaJewish General Hospital
Fundersnot available
KeywordsMedicineSalvage therapyLymphomaRefractory (planetary science)Diffuse large B-cell lymphomaOncologyTransplantationInternal medicineDiseaseIntensive care medicineChemotherapy

Abstract

fetched live from OpenAlex

Diffuse large B cell lymphoma (dlbcl) is an aggressive non-Hodgkin lymphoma, accounting for approximately 30% of lymphoma cases in Canada. Although most patients will achieve a cure, up to 40% will experience refractory disease after initial treatment, or relapse after a period of remission. In eligible patients, salvage therapy followed by high-dose therapy and autologous stem-cell transplantation (asct) is the standard of care. However, many patients are transplant-ineligible, and more than half of those undergoing asct will subsequently relapse. For those patients, outcomes are dismal, and novel treatment approaches are a critical unmet need. In this paper, we present available data about emerging treatment approaches in the latter setting and provide a perspective about the potential use of those approaches in Canada.

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)0.001
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.192
GPT teacher head0.453
Teacher spread0.262 · 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

Citations36
Published2019
Admission routes3
Has abstractyes

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