Abstract 2528: Rurality and neighborhood socioeconomic deprivation associated with patient-reported outcomes andsurvivalin men with prostate cancer in NRG RTOG 0415
Bibliographic record
Abstract
Abstract Background: Prostate cancer is the most common cancer among men. Monitoring of patient-reported outcomes (PROs) can enhance provider-patient communication, promote better decision-making, and improve survival. Geospatial factors, namely rurality and neighborhood socioeconomic deprivation, could influence the experience across cancer treatment, thus impacting PROs and survival. This study examined associations of rurality, neighborhood socioeconomic deprivation (Area Deprivation Index [ADI]) with cancer treatment-related PROs and survival in men with prostate cancer. Methods: Data from men with prostate cancer in the trial NRG Oncology/RTOG 0415 were analyzed. In this trial, 1092 men were randomized to receive conventional radiotherapy (RT) or hypofractionated RT. Patients had Gleason scores of 2-6 and prostate-specific antigen of <10 ng/mL. Rurality was categorized as urban vs rural using Rural-Urban Continuum Codes via patient zip codes. Neighborhood socioeconomic deprivation was assessed by the ADI calculated via the American Community Survey by patient zip codes. The Expanded Prostate Cancer Index Composite (EPIC) measures cancer-specific quality of life (QOL); Hopkins Symptom Checklist measures anxiety and depression; EuroQoL-5 Dimension (EQ-5D) VAS and Index scores assess general health. All PROs were measured at baseline before and 6, 12, 24 and 60 months after RT. Overall survival (OS) and disease-free survival (DFS) were assessed using Cox proportional hazards models. Mixed effects models and generalized estimating equations were used to analyze PROs. Results: We analyzed 751 patients with complete data. Patients from the ADI 25% most deprived neighborhoods (vs. the other 75% of neighborhoods) were more likely to be non-white, unmarried and from rural areas. At baseline, patients from the most deprived areas had worse EPIC bowel score (P=0.011), worse sexual score (P=0.042) and worse hormonal score (P=0.015); patients from the most deprived areas had worse self-care (P=0.04) and more pain (P=0.047). Patients from rural areas had worse EPIC urinary score (P=0.03) and sexual score (P=0.003). Longitudinal analyses showed that ADI 25% most deprived areas (β=4.05, P=0.001) and rural areas (β=-5.70, P=0.003) were associated with worse EQ-5D VAS score. Compared to the 75% less deprived neighborhoods and urban, the ADI 25% most deprived neighborhoods and rural areas respectively had 47% (HR=1.466, P=0.033) and 93% (HR=1.925, P=0.026) relative increase in risk of recurrence or death (DFS). No differences were seen in OS. Conclusions: Patients with prostate cancer from the most deprived neighborhoods and rural areas had low QOL at baseline, poor general health and DFS. Interventions should target populations from socioeconomic deprived neighborhoods and rural areas to improve patient access to supportive care services and DFS. Citation Format: Jinbing Bai, Stephanie L. Pugh, Ronald Eldridge, Katherine Yeager, Qi Zhang, W Robert Lee, Amit B. Shah, Ian S. Dayes, David P. D'Souza, Jeff M. Michalski, Jason A. Efstathiou, John M. Longo, Thomas M. Pisansky, Jordan M. Maier, Sergio L. Faria, Anand B. Desai, Samantha A. Seaward, Howard M. Sandler, Mary E. Cooley, Deborah W. Bruner. Rurality and neighborhood socioeconomic deprivation associated with patient-reported outcomes andsurvivalin men with prostate cancer in NRG RTOG 0415 [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2528.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".