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Record W4408808751 · doi:10.1080/14796694.2025.2479374

Treatment intensification in metastatic castration-sensitive prostate cancer: a real-world study in Alberta, Canada

2025· article· en· W4408808751 on OpenAlexaffabout
Steven Yip, Winson Y. Cheung, Armen Aprikian, Matthias Stoelzel, Kelvin Wong, A. Pranzo, Thomas W. McLean, Dylan E. O’Sullivan, Andrew Chilelli

Bibliographic record

VenueFuture Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsAlberta Health ServicesMcGill UniversityUniversity of Calgary
FundersPfizer
KeywordsMedicineProstate cancerDocetaxelInternal medicineBone metastasisAndrogen deprivation therapyOncologyMetastasisCancer

Abstract

fetched live from OpenAlex

AIM: To assess the current status of and factors associated with treatment intensification (TI) (with androgen receptor pathway inhibitors [ARPIs] and/or docetaxel) for metastatic castration-sensitive prostate cancer (mCSPC) in Canada. MATERIALS & METHODS: Retrospective analysis of data for 431 patients with mCSPC from the Alberta Prostate Cancer Research Initiative database (July 2014-March 2022). The primary objective was to assess the patient proportion receiving TI, time to TI, and associated factors. The secondary and exploratory objectives were evaluating TI patterns and factors associated with choice of therapy, respectively. RESULTS: Overall, 42% of patients received TI; most (65%) within 3 months post-index. TI was likely to occur within 3 months post-index in de novo mCSPC, but occurred later for recurrent mCSPC. Patients with recurrent mCSPC (HR [95% CI]: 0.52 [0.38-0.72]) and those aged ≥ 75 years (0.57 [0.36-0.93]) were less likely to receive TI. Patients with multiple metastatic sites and bone metastasis had a 2-3-fold higher likelihood of receiving TI. An ARPI was predominantly used (75%) for TI (median duration: 16.0 months). CONCLUSION: TI rates for mCSPC are suboptimal in Canada especially for older patients and those with recurrent mCSPC. TI prioritization in such groups may improve patient outcomes.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.066
Threshold uncertainty score1.000

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 head0.026
GPT teacher head0.377
Teacher spread0.351 · 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 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
Published2025
Admission routes2
Has abstractyes

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