Patient-reported functional outcomes and treatment-related regret in Hispanic and Spanish-speaking men following prostate cancer treatment
Bibliographic record
Abstract
OBJECTIVES: Compare functional outcomes and treatment-related regret over 10 years in Spanish- and English-speaking Hispanic men compared to non-Hispanic men following treatment of localized prostate cancer. METHODS AND MATERIALS: Data from a prospective cohort study of men with localized prostate cancer treated with active surveillance, radical prostatectomy or radiotherapy were used to examine the effect of survey language (Spanish speaking vs. English speaking) and ethnicity (Hispanic vs. non-Hispanic) on functional outcomes and treatment-related regret over 10 years. Outcomes were measured using validated questionaries adjusting for baseline patient and disease characteristics. RESULTS: A total of 770 men were included, 12% were Spanish-speaking and 12% were English-speaking Hispanic men. Compared to non-Hispanic men, Spanish-speaking Hispanic men had clinically meaningfully better urinary incontinence scores at years 3, 5 and 10 (adjusted mean difference [aMD], 12.4, 95% CI, 4.8 to 20.0; at year 10), as well as better bowel function scores at 10 years (aMD, 5.1, 95% CI 2.3 to 8.0). English-speaking Hispanic men had clinically worse urinary incontinence at 3 and 5 years (aMD, -10.7 [95% CI, -17.6 to -3.9]; at year 5) and bowel function at 10 years (aMD, -4.3 [95% CI, -8.2 to -0.4]) compared to Spanish-speaking Hispanic men. English-speaking Hispanic men were more likely to report regret than Spanish-speaking Hispanic men at 10 years (adjusted odds ratio, 7.9, 95% CI, 1.3-46.2). CONCLUSIONS: These findings underscore the importance of considering language and ethnicity when providing counseling and support for prostate cancer survivors, emphasizing the need for personalized patient-centered care.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".