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Record W3156107342 · doi:10.2217/fon-2021-0284

Health-Related Quality of Life and Pain with Selinexor in Patients with Advanced Dedifferentiated Liposarcoma

2021· article· en· W3156107342 on OpenAlexaff
Mrinal M. Gounder, Albiruni R. Abdul Razak, Adrienne M. Gilligan, Hoyee Leong, Xiwen Ma, Neeta Somaiah, Sant P. Chawla, Javier Martín‐Broto, Giovanni Grignani, Scott M. Schuetze, Bruno Vincenzi, Andrew J. Wagner, Bartosz Chmielowski, Robin L. Jones, Jatin J. Shah, Sharon Shacham, Michael Kauffman, Richard F. Riedel, Steven Attia

Bibliographic record

VenueFuture Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicSarcoma Diagnosis and Treatment
Canadian institutionsPrincess Margaret Cancer Centre
FundersNational Cancer InstituteKaryopharm Therapeutics
KeywordsMedicineQuality of life (healthcare)LiposarcomaIntensive care medicineMEDLINEPathologyNursingSarcoma

Abstract

fetched live from OpenAlex

Objective: Compare health-related quality of life (HRQoL) of selinexor versus placebo in patients with dedifferentiated liposarcoma. Materials & methods: HRQoL was assessed at baseline and day 1 of each cycle using the European Organization for Research and Treatment of Cancer 30-item core quality of life questionnaire. Results were reported from baseline to day 169 (where exposure to treatment was maximized while maintaining adequate sample size). Results: Pain scores worsened for placebo versus selinexor across all postbaseline visits, although differences in HRQoL at some visits were not significant. Other domains did not exhibit significant differences between arms; however, scores in both arms deteriorated over time. Conclusion: Patients treated with selinexor reported lower rates and slower worsening of pain compared with patients who received placebo.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.014
GPT teacher head0.289
Teacher spread0.275 · 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

Citations20
Published2021
Admission routes1
Has abstractyes

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