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Association between sociodemographic marginalization and receipt of intensified treatment for metastatic castrate-sensitive prostate cancer.

2025· article· en· W4407702707 on OpenAlexaffabout
Christopher J.D. Wallis, David‐Dan Nguyen, Raj Satkunasivam, Khatereh Aminoltejari, Amanda Hird, Soumyajit Roy, Scott C. Morgan, Shawn Malone, Bobby Shayegan, Girish S. Kulkarni, Quoc-Dien Trinh, Laura C. Rosella, Rodney H. Breau, Aly‐Khan A. Lalani

Bibliographic record

VenueJournal of Clinical Oncology · 2025
Typearticle
Languageen
FieldMedicine
TopicProstate Cancer Treatment and Research
Canadian institutionsMcMaster UniversitySt. Joseph’s Healthcare HamiltonJuravinski Cancer CentreSunnybrook Health Science CentreUniversity of OttawaOttawa HospitalPrincess Margaret Cancer CentreUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineProstate cancerOncologyCancerReceiptInternal medicineProstateGynecology

Abstract

fetched live from OpenAlex

96 Background: Intensified treatment beyond androgen deprivation therapy (ADT) has been shown to improve survival in patients with metastatic castrate-sensitive prostate cancer (mCSPC). However, real-world utilization remains inadequate. To assess potential differential access to such life-prolonging treatments, we assessed the association between sociodemographic marginalization and receipt of intensified treatment in patients newly diagnosed with mCSPC within a universal healthcare system. Methods: We performed a population-based cohort study of men aged 66 years or older diagnosed with de novo mCSPC in Ontario, Canada from Jan 2014-Nov 2021. Hierarchical regression models adjusting for patient, tumor, and physician characteristics were used to estimate odds ratios (OR) and 95% confidence intervals (CI) for the association between patient marginalization, measured by the Ontario Marginalization Index (ON-MARG), and receipt of intensified treatment, within 6 months of diagnosis. ON-MARG, a validated measure created using Canadian census data, provides area-level measures of marginalization in four domains: (1) households and dwellings; (2) material resources; (3) age and labour force; and (4) racialized and newcomer populations. Results: Among 6,051 patients, 1,465 (24%) received intensified treatment. Higher levels of composite marginalization were associated with a lower likelihood of receiving intensified treatment (OR 0.91, 95% CI 0.83–0.99, p=0.03). When examining individual domains of marginalization (Table 1), the racialized and newcomer populations domain showed the strongest negative association with the receipt of intensified treatment (OR 0.89, 95% CI 0.81–0.97, p=0.01). Conclusions: Sociodemographic marginalization, particularly among racialized and newcomer populations, is associated with lower rates of intensified treatment in patients with de novo mCSPC, even within a universal healthcare system, further contributing to known disparities in prostate cancer outcomes. Association between ON-MARG domains and treatment intensification among patients with de novo mCSPC in adjusted models.* ON-MARG Domain Effect Estimate (95% CI) Households & Dwellings OR 0.92 (0.85-0.98), p=0.02 Material Resources OR 0.93, (0.86-0.99), p=0.03 Age & Labour Force OR 0.99, (0.94-1.04), p=0.65 Racialized & Newcomer Populations OR 0.89, (0.81-0.97), p=0.01 *Each ON-MARG Domain was modelled in separate multivariable models adjusting for patient characteristics including age at diagnosis, Charlson comorbidity category, and area; tumor characteristics including Gleason score at diagnosis; as well as physician age, sex, years in practice, specialty, and group volume.

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)0.000
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 score0.412
Threshold uncertainty score0.820

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
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)0.0020.000

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 head0.176
GPT teacher head0.514
Teacher spread0.338 · 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".

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Citations0
Published2025
Admission routes2
Has abstractyes

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