PD46-07 CHANGES IN PATIENT REPORTED FINANCIAL TOXICITY AFTER RADICAL CYSTECTOMY
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVE: Racial disparities are known to affect urologic care.Previous studies of Peyronie's Disease (PD) have investigated responses to treatment by race, but not treatment rates themselves.Frequently, racial disparities in healthcare are attributed to differences in income or decreased insurance rates among people of color.For this reason, we aimed to investigate racial disparities in PD treatments by evaluating the VA Informatics and Computing Infrastructure (VINCI), which contains data from the Veteran Health Administration (VHA).As veterans are insured and able to obtain treatment for PD through the VHA, this population allows us to study the effects of race on treatment rates when insurance does not serve as a barrier.METHODS: We queried VINCI to obtain data for a cohort of men that have a recorded diagnosis of PD by ICD-9 or ICD-10 code between 2015-2020.We then used the following CPT codes to identify which patients had procedures known to be therapeutic for PD: plaque injection (CPT 54200), penile prosthesis (54400, 54405), penile reconstruction to correct penis angle (54360), or plaque excision (54110, 54111, 54112).We evaluated treatment rates among patients with race/ethnicity information recorded.We reported results as frequencies and used the chi-square test to evaluate significant differences in treatment rates when comparing racial groups to Whites, with statistical significance reported at p<.05.RESULTS: We obtained data for 17,022 patients with diagnoses of PD.Within the total population of patients diagnosed with PD who had race and ethnicity information recorded, 62.0% were White (Non-Hispanic), 24.6% were Black, 9.6% were Hispanic or Latino, 1.8% were Asian or Pacific Islander (API), and 2.0% were Native American (NA).Among this cohort, 8.1% of all patients, 9.5% of Whites, 6.0% of Blacks (p<.05), 5.4% of Hispanics (p<.05), 6.6% of API (p<.05), and 3.7% of NA (p<.05) were treated.CONCLUSIONS: Within the VHA population, which is not limited by access to health insurance, all racial groups were treated for PD at a significantly lower rate than Whites.Further studies are warranted to investigate the etiologies of these disparities, which may be due to variations in disease severity, cultural factors, or societal barriers.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".