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Association between patient-reported outcomes (PROs) and changes in prostate-specific antigen (PSA) in patients (pts) with advanced prostate cancer treated with apalutamide (APA) in the SPARTAN and TITAN studies.

2022· article· en· W4213306332 on OpenAlexaff
Eric J. Small, Kim N., Simon Chowdhury, Katherine B. Bevans, Amitabha Bhaumik, Fred Saad, Byung Ha Chung, Lawrence I. Karsh, Stéphane Oudard, Peter De Porre, Sabine Brookman‐May, Sharon McCarthy, Suneel Mundle, Hirotsugu Uemura, Matthew R. Smith, Neeraj Agarwal

Bibliographic record

VenueJournal of Clinical Oncology · 2022
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsCentre Hospitalier de l’Université de Montréal
FundersJanssen Research and Development
KeywordsMedicineProstate cancerOncologyInternal medicineProportional hazards modelTitan (rocket family)PlaceboAndrogen deprivation therapyPost-hoc analysisProstate-specific antigenProstateCancerUrologyPathology

Abstract

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73 Background: In phase 3 placebo (PBO)-controlled studies, addition of APA to androgen deprivation therapy (ADT) improved overall survival, resulted in rapid and deep PSA declines, and reduced risk of disease progression while preserving health-related quality of life (HRQoL) in nonmetastatic castration-resistant prostate cancer (nmCRPC; SPARTAN) and metastatic castration-sensitive prostate cancer (mCSPC; TITAN). This post hoc analysis evaluated the association of a deep PSA decline with PROs in these studies. Methods: Pts on ADT were randomized to APA (240 mg QD) or PBO: SPARTAN 2:1 (N = 1,207; APA n = 806), TITAN 1:1 (N = 1,052; APA n = 525). Each cycle was 28 d. PROs were assessed using Functional Assessment of Cancer Therapy-Prostate (FACT-P), Brief Pain Inventory-Short Form (BPI-SF; TITAN only), and Brief Fatigue Inventory (BFI; TITAN only) at baseline, specific cycles during study treatment, and post progression up to 1 yr. A landmark analysis at Month 3 evaluated association between deep PSA decline (≤ 0.2 ng/mL) and time to subsequent deterioration in PROs (defined as decrease ≥ 10 points FACT-P total, ≥ 3 points Physical Wellbeing, ≥ 30% baseline for BPI-SF worst pain, or ≥ 2 points for BFI worst fatigue). At time of the landmark analysis, only pts continuing treatment were included; all deep PSA responses after, and all PRO deterioration events before, were ignored. Time-to-event end points were analyzed by Kaplan-Meier method and Cox proportional hazards model. Results: Median treatment durations were 32.9 mo (SPARTAN) and 39.3 mo (TITAN). Per assessment, > 90% (SPARTAN, cycles 1-81) and > 50% (TITAN, cycles 1-33) of eligible pts completed FACT-P; BPI-SF and BFI, both > 62% (TITAN, cycles 1-33). Pts in either study who achieved PSA ≤ 0.2 ng/mL at Month 3 had a lower risk of deterioration in FACT-P total or Physical Wellbeing (Table). Pts in TITAN with PSA ≤ 0.2 ng/mL at Month 3 had a lower risk of BPI-SF worst pain intensity or BFI worst fatigue intensity progression (Table). Conclusions: Deep and rapid PSA responses with APA were associated with prolonged time to deterioration in HRQoL, FACT-P Physical Wellbeing, BPI-SF worst pain intensity, and BFI worst fatigue intensity in pts with advanced PC. Clinical trial information: NCT02489318 (TITAN); NCT01946204 (SPARTAN). [Table: see text]

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)0.004
metaresearch head score (Gemma)0.003
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.106
GPT teacher head0.438
Teacher spread0.333 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

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