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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 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: Non-randomized trial · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.021

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNon-randomized trial
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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