Kazmi, Syeda Zainab

144 Background: Androgen receptor pathway inhibitors (abiraterone acetate [AAP], apalutamide [APA], enzalutamide [E], darolutamide [DARO] have become the cornerstone for pts with PCa due to survival benefits. Comparative data on fracture risk among ARPi are limited. This study quantified the risk of bone fractures with ARPi plus androgen deprivation therapy (ADT) and compared fracture risk across different ARPi agents. Methods: MEDLINE and EMBASE were searched from inception through September 1 st 2025 to identify positive phase III trials assessing ARPi in addition to ADT in pts with PCa (m0CRPC, mCSPC, mCRPC). Trials were included if they reported bone fractures in either arm. Pairwise, random-effects meta-analysis was conducted using Paul-Mendel approach. Sensitivity analyses using Hartung-Knapp (HK) adjustment was performed to assess robustness of results. To assess the comparative risk of bone fractures among different ARPi agents, mixed treatment comparisons were made using frequentist network meta-analysis. P-scores were computed to assess relative treatment rankings. Treatment effect was expressed as odds ratio (OR) with 95% confidence intervals (CI). Results: This systematic review included 13 phase III trials with 15468 pts (8890 received ARPi+ADT, 6578 received ADT alone). A total of 596 (6.7%) bone fractures were observed with ARPi+ADT compared to 180 (2.74%) with ADT alone. Patients receiving ARPi+ADT were associated with an increased risk of bone fractures compared to those receiving ADT (OR: 2.40; 95% CI: 1.93-2.98). Sensitivity analyses with HK adjustment showed consistent results (2.40; 1.88-3.06). There was no statistically significant effect modification by PCa clinical setting (p: 0.27). Mixed treatment comparisons showed a statistically significant increased risk of bone fractures with AAP+ADT (2.35; 1.29-4.27), APA+ADT (2.04; 1.47-2.86), and E+ADT (3.13; 2.44-4.00) but not with DARO+ADT (1.30; 0.80-2.08) when compared to ADT alone. Among different ARPi agents, E+ADT was associated with an increased risk compared to APA+ADT (1.54; 1.01-2.33) and DARO+ADT (2.44; 1.41-4.17). Ranking analysis showed that E+ADT was ranked as potentially the least safe treatment (P score: 0.05) in terms of relative fracture risk followed by AAP+ADT (P score: 0.30), APA+ADT (P score: 0.43), DARO+ADT (P score: 0.76). Sensitivity analyses limiting to mCSPC trials only showed consistent results. Conclusions: The risk of bone fractures varies among different ARPi agents, with enzalutamide posing the highest relative risk and darolutamide the lowest. These differences may guide treatment selection, particularly in pts with mCSPC and bone metastases. Prospective studies adjusting for covariates are needed to evaluate bone-protective strategies to mitigate fracture risk particularly in mCSPC patients who are considering treatment with ARPi.

Abstract Background The ability of large language models (LLMs) to work collaboratively and screen studies in a systematic review (SR) is under-explored. Hence, we aimed to evaluate the effectiveness of LLMs in automating the process of screening in systematic reviews. Methods This is an observational study which included labeled data (title and abstracts) for five SRs. Originally, two reviewers screened the citations independently for eligibility. A third reviewer cross-checked each citation for quality assurance. GPT-4, Claude-3-Sonnet, and Gemini-Pro-1.0 were used using zero-shot chain-of-thought prompting. Collaborative approaches included (i): conflict resolution using benefit of the doubt, (ii) majority voting using an independent third LLM and (iii) conflict resolution using an informed third LLM. Performance was assessed using accuracy, precision for exclusion, and recall for inclusion. Work saved over samples (WSS) was computed to estimate the reduction in manual human effort. Results A total of 11300 articles were included in this study. The individual models, GPT-4, Claude-3-Sonnet, and Gemini-Pro-1.0 exhibited a high precision for exclusion, achieving 99.7%, 99.7%, and 99.2% and high recall for inclusion achieving 95.5%, 96.6% and 85.7%, respectively. However, the collaborative approach utilizing the two best-performing models (GPT-4 and Claude-3S) achieved an average precision of 99.9% and a recall of 98.5% (across all collaborative approaches). Furthermore, the proposed collaborative approach resulted in an average WSS of 63.5%, compared to the average WSS of 45.2% for individual models. Conversational LLM interactions showed a consistent pattern of results. Limitations This study was limited due to reliance on proprietary models, and evaluation on oncology datasets. Conclusion Evidence shows that collaborative LLMs enable efficient, high-performing screening in systematic reviews, supporting continuous evidence updates. Primary funding source NIH (U24CA265879-01-1) and Carolyn-Ann-Kennedy-Bacon Fund.

116 Background: The impact of socioeconomic status on genitourinary (GU) cancer mortality in the United States (US) is unclear. Hence, we evaluated the association of the socioeconomic status with GU cancer mortality across the US counties. Methods: County-level age-adjusted mortality rates (AAMR) per 100,000 person-years (PY) for populations diagnosed with GU cancers were abstracted from the wide-ranging online data for epidemiological research (WONDER) database. The agency for toxic substances and disease registry (ATSDR) was queried to obtain county-level social vulnerability indices from 2014-2018. Percentile ranking scores (PRS: ranging from 0-1) were computed for each US county and further categorized into quartiles (Q: 1st: 0-0.25 [least vulnerable]; 4th: 0.75-1.00 [most vulnerable]). AAMRs were then linked with quartile rankings. A population-weighted, Poisson regression analysis was conducted to compute rate ratios (RR) of AAMRs between 4th and 1st Q with the corresponding 95% confidence intervals (CI). Results: A total 3142 US counties were included in this analysis. The AAMR for GU cancers was 22.6 overall deaths (OD) and 4.20 premature deaths (PD; defined as death at age <65 years) per 100,000 PY. GU cancer-related mortality increased in a stepwise fashion from the 1st Q to the 4th Q (OD: 21.6 vs 24.1; PD: 3.48 vs 5.19). In terms of OD by specific GU cancers, significantly higher AAMRs were observed in the 4th Q compared to the 1st Q for patients with prostate cancer (RR; 1.19 [95% CI;1.14-1.24]), and renal cell carcinoma (RCC; 1.12 [1.04-1.21]) but not for those with lower urothelial tract cancers (0.92 [0.87-0.96]). Among subgroups, non-Hispanic Blacks (1.19 [1.07-1.33]), and Hispanics (1.28 [1.08-1.51]) with prostate cancer and men with RCC (1.11; 1.03-1.19) in the 4th Q experienced significantly higher overall mortality when compared to those in the 1st Q. With regards to PD, significantly higher AAMRs were observed in the 4th Q vs 1st Q for patients with prostate cancer (1.54 [1.34-1.77]), and RCC (1.28 [1.12-1.45]). Consistently, men with RCC in the 4th Q also experienced higher premature mortality when compared to the 1st Q. Sparsity of mortality data among different ethnic/racial subgroups across the US counties precluded any formal assessment of disparity by specific GU cancers. A sensitivity analysis, conducted using the weighted linear regression approach, showed consistent results. Conclusions: Population level data suggest that socially vulnerable populations, especially Non-Hispanic Black and Hispanic men with prostate cancer, and men with renal cell carcinoma may be at an increased risk of cancer-related mortality. Hence, there is a dire need to address this mortality gap across different socioeconomic subgroups to ensure health-care equity. Investigations at the patient level are required to further ascertain these findings.

499 Background: The impact of socioeconomic status on gastrointestinal (GI) cancer mortality in the United States (US) is not well-established. We hypothesized that socially vulnerable populations have disproportionately higher mortality rates. Hence, we assessed the association of the social vulnerability index (SVI) with GI cancer mortality across US counties. Methods: Social vulnerability indices were obtained from the agency for toxic substances and disease registry (ATSDR) from 2014-2018 to compute percentile ranking scores (PRS: ranging from 0-1) for each US county. PRS were further categorized into quartiles (Q: 1st: 0-0.25 [least vulnerable]; 4th:0.75-1.00 [most vulnerable]). The wide-ranging online data for epidemiological research (WONDER) database was queried to abstract county-level age-adjusted mortality rates (AAMR) per 100,000 person-years (PY) for populations diagnosed with GI cancers. AAMRs were then linked with quartile rankings. Rate ratios (RR) of AAMRs between 4th and 1st Q were subsequently estimated with 95% confidence intervals using population-weighted, Poisson regression. Results: A total of 3142 counties were included in this analysis. The AAMR for overall deaths (OD) and premature deaths (PD; defined as death at age <65) in the GI cancer population was 53.8 and 17.7 per 100,000 PY. A gradient increase in GI cancer-related mortality was observed from 1st Q to 4th Q (OD: 49.2 vs 59.3; PD: 14.1 vs 21.3). This stepwise increase in AAMR over the quartiles was consistent across gender, different racial/ethnic subgroups, and rural/urban categories of counties. The AAMRs for OD were significantly higher in the 4th Q as compared to the 1st Q for gastric (RR: 1.67 [95% CI, 1.51-1.86]), hepatocellular including intrahepatic biliary (1.52 [1.45-1.61]), colorectal (1.21 [1.16-1.26]) and biliary (1.17 [1.06-1.28]) cancer. Similarly, significantly higher AAMRs for PD were observed in 4th Q vs1st Q for gastric (1.81 [1.60-2.05]), hepatocellular including intrahepatic biliary (1.78 [1.58-2.00]), biliary (1.64 [1.35-1.99]), colorectal (1.29 [1.21-1.39]) and pancreatic (1.16 [1.07-1.25]) cancer. However, no significant differences were observed for esophageal and small intestinal cancers. Men with gastric (1.58 [1.41-1.78]) and hepatocellular cancer (1.54 [1.43-1.66]), non-Hispanic Black population (1.54 [1.31-1.80]) with hepatocellular cancer, and Hispanic population with colorectal cancer (1.50 [1.31-1.72]) were observed to have higher overall mortality in the 4th Q compared to the 1st Q. The findings were similar for premature mortality. Conclusions: Population-level data suggests that the US counties with higher socio-economic adversities may be at an increased risk of GI cancer-related mortality. Investigations using patient-level data are required to probe the impact of socioeconomic vulnerabilities on cancer-related mortality.