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Record W3026687938 · doi:10.2991/chi.d.200502.001

Maintenance Strategies Post-Autologous Stem Cell Transplantation for Newly Diagnosed Multiple Myeloma

2020· review· en· W3026687938 on OpenAlexaff
Sarah Bird, Graham Jackson, Charlotte Pawlyn

Bibliographic record

VenueClinical Hematology International · 2020
Typereview
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsInstitute of Cancer Research
FundersNational Institute for Health and Care Research
KeywordsMedicineMultiple myelomaAutologous stem-cell transplantationStem cellMaintenance therapyTransplantationIntensive care medicineDiseaseOncologyMalignancyChemotherapyInternal medicine

Abstract

fetched live from OpenAlex

Multiple myeloma, the second most common hematological malignancy worldwide, has demonstrated dramatic improvements in outcome in the last decade. In newly diagnosed patients, induction chemotherapy followed by autologous stem cell transplantation (ASCT) is the standard of care. After ASCT, the majority of patients experience disease remission but, despite recent therapeutic developments, most will eventually relapse. In this review we consider clinical aspects of maintenance therapies that can be used post-ASCT to prolong remission duration. We discuss the evidence for the effectiveness of each of these drugs as a maintenance therapy, alongside other benefits and drawbacks to their use, for example, route of administration and potential toxicities. We discuss questions which remain unanswered around the optimal use of currently available maintenance therapies and review newer agents being considered for use as maintenance such as emerging immunotherapies.

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)0.000
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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.096
GPT teacher head0.433
Teacher spread0.337 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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