Association of marginalization and PSMA-PET in prostate cancer
Bibliographic record
Abstract
INTRODUCTION: Prostate-specific membrane antigen-positron emission tomography (PSMA-PET) is a new standard for the imaging of high-risk or recurrent prostate cancer. While marginalization disparities exist for prostate cancer, less is known in the context of PSMA-PET. The objective of the study was to determine if marginalization was associated with access, PET positivity, management change, radiation use, and survival of prostate cancer in a universal healthcare system. METHODS: Patients enrolled in the Ontario PSMA-PET Registry for Recurrent Prostate Cancer (PREP) between 2018 and 2022 were included. The Ontario Marginalization Index (material resources, racialized/newcomer, age/labor force, household/dwellings) was used. Outcomes included access, PET positivity, management change, radiation use, and survival. Cox proportional hazards and logistic regression models examined the association between marginalization and outcomes. Provincial administrative databases were leveraged to generate a diagnosis and a survivorship cohort of prostate cancer patients who received primary treatment to compare with the PSMA-PET cohort. RESULTS: There were 4034 patients in the PSMA-PET cohort. Patients at higher material marginalization quintiles were under-represented in the PSMA-PET Registry Database. Similar under-representation was noted in the diagnosis (n=123 128) and survival (n=56 753) cohorts. Within the PSMA cohort, marginalization dimensions were not significantly correlated with PET positivity, management change, or radiation use. CONCLUSIONS: Marginalization quintiles across PSMA-PET access were similar in distribution to prostate cancer diagnoses and survivor cohorts. We found no association of marginalization with PET positivity, management change, or radiation use among those receiving PSMA-PET.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".