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Comparable outcome of stem cell transplant versus bortezomib-based consolidation in myeloma patients after major response to induction

2013· article· en· W2067704026 on OpenAlexaff
Wen Gao, Chuanying Geng, Lei Zhang, Yanchen Li, Wenming Chen, Chen Wang

Bibliographic record

VenueHematology · 2013
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of TorontoMount Sinai Hospital
Fundersnot available
KeywordsMedicineBortezomibMultiple myelomaAutologous stem-cell transplantationOncologyInternal medicineRetrospective cohort studySurgery

Abstract

fetched live from OpenAlex

High-dose therapy with autologous stem cell transplant (ASCT) has been established as standard treatment for eligible patients with myeloma. However, whether this approach is still beneficial with new therapy is yet to be determined. Consolidation of effective therapy may be an alternative to ASCT following major response to initial induction. This retrospective case-series analysis included a total of 48 patients with newly diagnosed myeloma. All these patients achieved complete response or very good partial response to bortezomib-based induction and were eligible for ASCT; 24 of these patients proceeded with ASCT, and other 24 patients opted out of ASCT and received two additional cycles of bortezomib therapy as consolidation. With a median follow-up of 28.5 months in ASCT group and 29 months in non-ASCT group, no significant difference was seen in progression-free survival, 39 versus 32 months, P = 0.82. Median overall survival had not been reached, and the estimated 3-year overall survival rates were 87.5 and 67.5% in ASCT and non-ASCT, respectively, P = 0.97. This study provides an initial assessment of survival outcome of ASCT in comparison with non-ASCT consolidation. The additional study is required to establish the efficacy of non-ASCT consolidation.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.047
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

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

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 head0.036
GPT teacher head0.308
Teacher spread0.272 · 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 teacher head, 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

Citations1
Published2013
Admission routes1
Has abstractyes

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