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588 Dostarlimab + chemotherapy for the treatment of primary advanced or recurrent endometrial cancer (pA/rEC) in the RUBY trial: post hoc analysis of the costs of grade ≥3 adverse events (AEs)

2023· article· en· W4388048382 on OpenAlexaff
Solomon J. Lubinga, Tilman Payer, Meghann Gregg, Lydia Lee, Odette Allonby, Llenalia García-Fernández, Jean Hurteau

Bibliographic record

VenueRegular and Young Investigator Award Abstracts · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsGlaxoSmithKline (Canada)
FundersU.S. National Library of Medicine
KeywordsPlaceboMedicinePost-hoc analysisCarboplatinInternal medicineOncologyUrologyChemotherapyCisplatin

Abstract

fetched live from OpenAlex

<h3>Background</h3> In the RUBY trial (NCT03981796), dostarlimab + carboplatin-paclitaxel (CP) significantly increased progression-free survival (PFS) compared with placebo+CP in patients with pA/rEC. Grade ≥3 AEs were more frequent with dostarlimab+CP vs placebo+CP. This analysis estimated the difference in per-patient costs of grade ≥3 AEs with dostarlimab+CP vs placebo+CP. <h3>Methods</h3> This AE cost model used grade ≥3 treatment-emergent AE (TEAE) and treatment-related AE (TRAE) data from RUBY part 1. Grade ≥3 AEs are likely to require specialized clinical treatment and/or hospitalization; as a surrogate, management costs were extracted from the US Healthcare Cost and Utilization Project using 2020 inpatient hospitalization data. In the base-case analysis, mean per-patient costs for each AE were calculated by multiplying management cost by the number of AEs observed and dividing by the number of participants. In a scenario analysis, number needed to treat to harm (NNTH) or benefit (NNTB) was derived from the risk difference for each AE, and mean per-patient cost differences were calculated by dividing management cost by NNTH or NNTB. All analyses were performed in the mismatch repair-deficient (dMMR)/microsatellite instability-high (MSI-H) and intention-to-treat (ITT) populations. <h3>Results</h3> In the base-case analysis in the dMMR/MSI-H group, aggregate per-patient costs were $26,968 (US$) with dostarlimab+CP vs $35,862 with placebo+CP (difference: −$8,894) for TEAEs (<b>figure 1</b>) and $19,775 vs $26,005, respectively, (difference: −$6,230) for TRAEs (<b>figure 2</b>). Lower predicted AE costs for dostarlimab+CP vs placebo+CP were driven by higher costs of managing decreases in neutrophil and white cell counts, which occurred more frequently in the placebo arm. In the ITT population, aggregate per-patient costs were $28,199 with dostarlimab+CP vs $25,219 with placebo+CP (difference: $2,980) for TEAEs and $19,375 vs $19,156, respectively, (difference: $219) for TRAEs. The higher predicted costs for dostarlimab+CP in the ITT population were driven by comparatively smaller differences in costs of neutrophil and white cell count decreases but higher costs of anemia, sepsis, peripheral neuropathy, and metabolic enzyme derangements. AE cost differences were qualitatively similar in the scenario analysis. <h3>Conclusions</h3> In the dMMR/MSI-H population, grade ≥3 TEAE and TRAE costs were predicted to be substantially lower for dostarlimab+CP. In the ITT population, grade ≥3 TEAE costs were predicted to be somewhat higher for dostarlimab+CP, while grade ≥3 TRAE costs were similar between arms. Together with the significant PFS benefits, these results further support the use of dostarlimab+CP as a new standard of care, especially in patients with dMMR/MSI-H pA/rEC. <h3>Acknowledgements</h3> This study was funded by GSK. Medical editorial assistance was provided by ArticulateScience, LLC, and was funded by GSK. <h3>Trial Registration</h3> U.S. National Library of Medicine ClinicalTrials.gov, NCT03981796

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.312
Threshold uncertainty score0.414

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.292
Teacher spread0.267 · 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 designBench or experimental
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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