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Record W4307092490 · doi:10.1080/03007995.2022.2139052

Updated results from a matching-adjusted indirect comparison of efficacy outcomes for ciltacabtagene autoleucel in CARTITUDE-1 versus idecabtagene vicleucel in KarMMa for the treatment of patients with relapsed or refractory multiple myeloma

2022· article· en· W4307092490 on OpenAlexaff
Tom Martin, Saad Z. Usmani, Jordan M. Schecter, Tito Roccia, Carolyn C. Jackson, William Deraedt, Tzu‐Min Yeh, Arnob Banerjee, Lida Pacaud, Ashraf Garrett, Meaghan Bartlett, Anja Haltner, Suzy Van Sanden, Joris Diels, Satish Valluri, Imtiaz A. Samjoo

Bibliographic record

VenueCurrent Medical Research and Opinion · 2022
Typearticle
Languageen
FieldMedicine
TopicMultiple Myeloma Research and Treatments
Canadian institutionsEVERSANA (Canada)
Fundersnot available
KeywordsMedicineInternal medicineConfidence intervalHazard ratioOdds ratioGastroenterologyOncology

Abstract

fetched live from OpenAlex

OBJECTIVE: CAR-positive T-cells in the treatment of patients with relapsed or refractory multiple myeloma (RRMM) who were previously treated with a proteasome inhibitor, an immunomodulatory drug, and an anti-CD38 monoclonal antibody (i.e. triple-class exposed). METHODS: MAICs were performed with the latest available individual patient data for cilta-cel (CARTITUDE-1) and published summary-level data for ide-cel (KarMMa). The analyses included treated patients from CARTITUDE-1 who satisfied the eligibility criteria for KarMMa. The MAIC adjusted for unbalanced baseline covariates of prognostic significance identified in the literature and by clinical expertise. Comparative efficacy was assessed for overall response rate (ORR), complete response or better (≥CR) rate, duration of response (DoR), progression-free survival (PFS), and overall survival (OS). RESULTS: = .0200]) compared with ide-cel. CONCLUSIONS: These analyses demonstrate improved efficacy with cilta-cel versus ide-cel for all outcomes over longer follow-up and highlight its therapeutic potential in triple-class exposed RRMM patients.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.362
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.183
GPT teacher head0.448
Teacher spread0.265 · 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

Citations25
Published2022
Admission routes1
Has abstractyes

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