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Practice patterns and predictors of treatment intensification in patients with metastatic castration-sensitive prostate cancer.

2023· article· en· W4324136820 on OpenAlexaffabout
Geoffrey Gotto, Steven Yip, Bobby Shayegan, Dylan E. O’Sullivan, Christopher J.D. Wallis, Naveen S. Basappa, Ilias Cagiannos, Robert J. Hamilton, Cristiano Ferrario, Ricardo Fernandes, Brita Danielson, Fred Saad, Sebastién J. Hotte, Darren R. Brenner, Winson Y. Cheung, Devon J. Boyne, Katherine Chan, B. Osborne, Anousheh Zardan, Shawn Malone

Bibliographic record

VenueJournal of Clinical Oncology · 2023
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsCentre Hospitalier de l’Université de MontréalLondon Health Sciences CentreWestern UniversityUniversity of TorontoUniversity of CalgaryMount Sinai HospitalPrincess Margaret Cancer CentreOttawa HospitalJewish General HospitalMcGill UniversityUniversity of OttawaMcMaster UniversityUniversity of AlbertaJuravinski Cancer CentreSt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsEnzalutamideMedicineAbiraterone acetateDocetaxelProstate cancerAndrogen deprivation therapyOncologyInternal medicinePopulationCohortCabazitaxelCancerAndrogen receptor

Abstract

fetched live from OpenAlex

76 Background: In recent years, treatment intensification beyond androgen deprivation therapy (ADT) with several novel therapies have shown survival benefit in patients with metastatic castration-sensitive prostate cancer (mCSPC). Given the rapidly evolving landscape in mCSPC treatment, there is a need to better understand how treatment strategies fit in real-world clinical practice and are combined/sequenced with other available therapies. Methods: Using electronic medical records and administrative data, a population-based retrospective cohort study was conducted. Patients aged ≥18 years of age who were newly diagnosed with de novo mCSPC and initiated ADT post-diagnosis between 2010 to 2020 in Alberta, Canada, were included. Treatment intensification was defined as the receipt of apalutamide, abiraterone acetate, enzalutamide, or chemotherapy (e.g. docetaxel) within 180 days of ADT initiation. Results: A total of 2,515 de novo mCSPC were identified during study period with 2,098 (83%) patients initiating ADT post-diagnosis. Of those, 525 (25%) received intensification beyond ADT. The percentage of patients who were intensified was 3% in 2010-2013 and gradually increased to 67% in 2020. Between 2014-2017, docetaxel was the most common therapy for intensification, but its use decreased considerably in 2018-2020 with abiraterone acetate, apalutamide and enzalutamide becoming increasingly available in the mCSPC setting. Upon progression, 46% and 22% in the intensified group versus 38% and 13% in the ADT-alone group initiated one and two-lines of subsequent therapies respectively. Abiraterone acetate and enzalutamide were the most common subsequent therapy for both the intensified (32% and 31% respectively) and the ADT-alone (56% and 38% respectively) groups. Docetaxel (24%) was used as subsequent therapy among mCSPC patients who were intensified with oral systemic agents. In multivariable logistic regression analyses of patients diagnosed in 2014-2020, significant predictors of intensification were younger age at diagnosis, lower Charlson comorbidity index, greater number of metastatic sites, shorter time to ADT initiation, referral to a specialists/cancer centres, surgery or radiation prior to ADT, and more recent year of diagnosis (all p<0.05). Conclusions: In Alberta, Canada, there has been a considerable increase in the utilization of ADT intensification therapies that correspond with the timing of clinical trial data and approvals of novel agents. Early referral to specialists/cancer centres is warranted to intensify mCSPC treatment beyond ADT and to improve patients’ outcomes. [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)0.001
metaresearch head score (Gemma)0.006
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 score0.118
Threshold uncertainty score0.235

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.128
GPT teacher head0.489
Teacher spread0.361 · 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
Published2023
Admission routes2
Has abstractyes

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