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The Use of Novel Agents In Patients with Multiple Myeloma Initially Treated with Allogeneic Stem Cell Transplantation Results In A Significant Prolongation of Post Relapse Survival.

2010· article· en· W2567835463 on OpenAlexaff
Christopher P. Venner, Heather J. Sutherland, John D. Shepherd, Yasser Abou Mourad, Michael J. Barnett, Donna L. Forrest, Donna E. Hogge, Stephen H. Nantel, Sujaatha Narayanan, Thomas J. Nevill, Janet Nitta, Maryse Power, Cynthia L. Toze, Clayton A. Smith, Kevin Song

Bibliographic record

VenueBlood · 2010
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineMultiple myelomaCohortTransplantationPopulationInternal medicineHematopoietic stem cell transplantationSurgeryDonor lymphocyte infusionProgression-free survivalMedian follow-upOncologyOverall survival

Abstract

fetched live from OpenAlex

Abstract Abstract 4580 Background: The use of allogeneic hematopoietic stem cell transplant (alloHSCT) in the treatment of Multiple Myeloma (MM) remains controversial. Although there is hope that alloHSCT may result in a cure, relapse continues to be a significant problem. The morbidity associated with late complications of allogeneic transplantation further compounds the issues faced when addressing relapsed disease. The use of Novel Agents (NA) in this patient population has been poorly characterized. Here we present our experience of NA use in patients initially treated with alloHSCT. Patients: 108 patients underwent an allografting procedure for their MM at our center between 1989 and 2009. 84 received a fully myeloablative procedure (15 received donor lymphocyte infusion). 24 received an autologous HSCT followed by a reduced intensity allogeneic procedure. 56 have relapsed with this population making up our primary cohort for analysis. 22 patients received NAs and very few patients received them prior to transplant (4/108). Endpoints examined were post relapse survival after the initial HSCT procedure (PRS), overall survival from time of initial treatment (OS) and progression free survival (PFS) measured in months (m). Results: Of the entire cohort of 108 patients median OS was 78.6m (95% CI; 24.5–132.6). Median PFS was 23.6m (95% CI; 15.4–31.8). Of the non-relapsed patients (n = 52) the median OS was 125.9m. In this cohort 67% of the deaths occurred within 1.5 years. Of the relapsed patients (n = 56) median PFS was 18.7m (95% CI; 14.6–22.8), median PRS was 31.5m (95% CI; 17.0–46.0), and median OS was 67.0m (95% CI; 31.6–102.5). The effect of NA was examined in the cohort of relapsed patients. No significant difference was noted in PFS between those exposed to NA and those who were not exposed (19.0m (95% CI; 10.1–22.8) vs 13.7m (95% CI; 5.8–21.6); p = 0.27). Exposure to NA showed improvements in PRS (42.3m (95% CI; 7.3–77.2) vs 10.4m (95% CI; 5.2–15.7); p = 0.01, Figure 1). A trend toward superior OS was noted (71.4m (95% CI; 37.9–105.5) vs 24.6m (95% CI; 3.0–46.1); p = 0.11) although this did not reach statistical significance. Conclusion: Ongoing management of relapsed patients with multiple myeloma in the post alloHSCT setting remains a significant challenge. This retrospective study demonstrates that the use of NA is both safe and effective in treating relapsed disease. The predominant impact of these drugs is seen in the relapsed setting. Exposure to NA correlates with a 22m improvement in PRS. A 46m improvement in OS is noted however, likely due to the small cohort, it failed to reach statistical significance. Disclosures: Sutherland: Celgene: Honoraria; Orthobiotech: Honoraria. Shepherd:Celgene: Honoraria; Orthobiotech: Honoraria. Nevill:Celgene: Honoraria. Toze:Hoffman La Roche: Consultancy, Honoraria, Research Funding; Genzyme: Honoraria, Research Funding; Glaxo Smith Kline: Honoraria.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.037
GPT teacher head0.255
Teacher spread0.217 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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