MétaCan
Menu
← Back to cohort

High-Risk Cytogenetics Multiple Myeloma: Impact of Consolidation and Maintenance

2016· article· en· W2595917680 on OpenAlexaff
Víctor H. Jiménez‐Zepeda, Peter Duggan, Paola Neri, Jason Tay, Fariborz Rashid-Kolvear, Nizar J. Bahlis

Bibliographic record

VenueBlood · 2016
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsCalgary Laboratory ServicesInstitute of Cancer ResearchUniversity of Calgary
Fundersnot available
KeywordsMedicineBortezomibMultiple myelomaExact testInternal medicineLog-rank testOncologySurgerySurvival analysis

Abstract

fetched live from OpenAlex

Abstract Introduction MM is a very heterogeneous disease for which several new treatments have become available over the past decade. With the advent of novel agents, the outcomes of this disease have improved dramatically. Unfortunately, High-risk myeloma (HRM) defined by the presence of del(17p), t(4;14), t(14;16), del13q by conventional karyotype and hypodiploidy, continues to exhibit poorer outcomes. Based on the above mentioned, we aimed to assess the clinical outcomes of patients with HRM treated at our center. Methods All consecutive HRM patients who underwent single auto-SCT at Tom Baker Cancer Center (TBCC) from 01/2004 to March/2016 were evaluated. HRM was defined by FISH and conventional karyotype when available. Two-sided Fisher exact test was used to test for differences between categorical variables. A p value of <0.05 was considered significant. Survival curves were constructed according to the Kaplan-Meier method and compared using the log rank test. All statistical analyses were performed by using the SPSS 22.0 software. Results 73 consecutive patients with HRM underwent single auto-SCT at our Institution over the defined period. Clinical characteristics are shown in Table 1. Eighty-seven percent of patient received bortezomib-containing regimens as induction regimens. Day-100 response post-ASCT is seen in Table 1. Consolidation was given to 41.7% and maintenance to 79% of cases. At the time of analysis, 43 patients are still alive and 40 have already progressed. Median OS and PFS were 50.8 and 21.9 months, respectively for the whole group. Median OS was 50.4 months for the group receiving consolidation compared to 39 months for those without (p=0.1). In addition, median PFS was longer in the group treated with consolidation (NR, Estimate 25 months vs 13.5 months, p=0.02, Fig1a). Furthermore, OS and PFS were longer in the group receiving some form of maintenance compared to those without (56.3 and 22.5months vs 19.9 and 9 months, p=0.04 and 0.01, respectively) (Fig 1b and c). In conclusion, HRM is an aggressive form of myeloma where the OS and PFS are shorter than the standard risk MM. Consolidation and maintenance strategies seemed to increase both OS and PFS in our current report, but clinical outcomes are still poor. Novel strategies such as immune modulation,check-point inhibition, among others are needed to maximize the impact of the consolidation and maintenance phases in this group of patients. Progression-Free Survival and consolidation Progression-Free Survival and consolidation Figure 1 Overall survival and maintenance Figure 1. Overall survival and maintenance Figure 2 Progression-Free survival and maintenance Figure 2. Progression-Free survival and maintenance Disclosures Jimenez-Zepeda: Janssen: Honoraria; Amgen: Honoraria; Takeda: Honoraria; Celgene, Janssen, Amgen, Onyx: Honoraria. Neri:Celgene and Jannsen: Consultancy, Honoraria. Bahlis:Amgen: Consultancy, Honoraria; BMS: Honoraria; Onyx: Consultancy, Honoraria; Celgene: Consultancy, Honoraria, Other: Travel Expenses, Research Funding, Speakers Bureau; Janssen: Consultancy, Honoraria, Other: Travel Expenses, Research Funding, Speakers Bureau.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0020.000

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 head0.018
GPT teacher head0.289
Teacher spread0.271 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueBlood→Same topicMultiple Myeloma Research and Treatments→French-language works237,207→