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Record W4415763898 · doi:10.1038/s41391-025-01047-7

DARolutamide ObservationaL (DAROL) study in patients with nonmetastatic castration-resistant prostate cancer: prespecified third interim analysis

2025· article· en· W4415763898 on OpenAlexaff
Evan Y. Yu, Hiroyoshi Suzuki, Christopher Pieczonka, Geoffrey Gotto, Alberto Briganti, Murilo Luz, Declan G. Murphy, Ryan J. Malone, Joelle Hamilton, Jonathan E. Chan, Paul Sieber, Robert Given, Eva Hellmis, T. Kretz, Philipp Spiegelhalder, Antonio Gómez‐Caamaño, Yaovi Messan Amela, Xavier Artignan, Hiroji Uemura, Naoki Fujita, Patrick Adorjan, Mercedeh Ghadessi, Frank Verholen, Andrew J. Armstrong

Bibliographic record

VenueProstate Cancer and Prostatic Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsUniversity of TorontoUniversity of Calgary
FundersBayer HealthCareBayer
KeywordsInterim analysisProstate cancerObservational studyProstateBenign prostatic hyperplasia (BPH)InterimClinical trialExploratory analysis

Abstract

fetched live from OpenAlex

BACKGROUND: DAROL is an ongoing study of real-world safety and effectiveness of darolutamide in patients with nonmetastatic castration-resistant prostate cancer (nmCRPC). SUBJECTS/METHODS: This prespecified interim analysis included 550 patients with nmCRPC who completed ≥6 months of treatment with darolutamide 600 mg twice daily. RESULTS: Darolutamide showed consistent safety and effectiveness in DAROL vs ARAMIS. Most treatment-emergent adverse events were grade 1/2. Two-year overall survival and metastasis-free survival rates and prostate-specific antigen responses were similar to ARAMIS. CONCLUSIONS: These findings indicate that darolutamide offers effectiveness and a favorable safety profile in the broad range of patients seen in clinical practice.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
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 head0.032
GPT teacher head0.344
Teacher spread0.312 · 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.

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

Citations1
Published2025
Admission routes1
Has abstractyes

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