Supplementary Figure 1. Forest plot of prevalence of breast cancer screening among cancer survivors. The estimated prevalence of breast cancer screening for each study is presented numerically and graphically, with point estimates shown as boxes scaled by study weight and whiskers representing the 95% confidence intervals (CIs). The overall pooled prevalence, calculated using a random-effects model, is represented by a diamond, with its width corresponding to the 95% CI.

Supplementary Figure 4. Pooled prevalence of breast cancer screening by study design. The pooled prevalence estimates of breast cancer screening, calculated using random-effects models, are displayed as diamonds with whiskers indicating 95% confidence intervals (CIs). Estimates were pooled separately by study design (cross-sectional vs. case-control vs. cohort), using the chi-square statistic to test for differences in pooled estimates across subgroups (p=0.344).

Supplementary Figure 12. Funnel plot of odds ratio for breast cancer screening among cancer survivors compared to the cancer-free general population. The outer dashed lines indicate the triangular region within which 95% of studies are expected to lie in the absence of bias. The solid black line corresponds to the summary effect estimate. Black dots correspond to studies included in this analysis.

Supplementary Table 3. Study quality assessment for both study aims. Table 3A displays the study quality for each study included in Aim 1, while Table 3B (case-control studies) and Table 3C (cross-sectional studies) display the study quality for each study in Aim 2.

Supplementary Figure 3. Pooled prevalence of breast cancer screening by screening method. The pooled prevalence estimates of breast cancer screening, calculated using random-effects models, are displayed as diamonds with whiskers indicating 95% confidence intervals (CIs). Estimates were pooled separately by method of screening, categorized as mammography only, mammography plus other screening method, or other non-mammography screening method only (including clinical breast examination, magnetic resonance imaging, or breast sonography). The chi-square statistic was used to test for differences in pooled estimates across subgroups (p=0.450).

Supplementary Figure 5. Pooled prevalence of breast cancer screening by study quality. The pooled prevalence estimates of breast cancer screening, calculated using random-effects models, are displayed as diamonds with whiskers indicating 95% confidence intervals (CIs). Estimates were pooled separately based on study quality (low, moderate or high, assessed using revised version of the tool proposed by Hoy et al. (33)). The chi-square statistic was used to test for differences in pooled estimates across subgroups (p=0.509).

Supplementary Figure 19. Pooled odds ratio of breast cancer screening comparing cancer survivors to the cancer-free general population, stratified by period of assessment. The odds ratio (OR) reported in each study is presented numerically and graphically, with point estimates shown as boxes scaled by study weight and whiskers representing the 95% confidence intervals (CIs). Estimates were pooled separately according to whether studies assessed screening starting prior to or after 2010. The chi-square statistic was used to test for differences in pooled estimates across subgroups (p=0.620). Pooled ORs, calculated using a random-effects model, are represented by diamonds with their width corresponding to the 95% CI.

Supplementary Figure 14. Sensitivity analysis for meta-analysis of the odds ratio for breast cancer screening excluding studies examining breast cancer screening other than screening mammography. The odds ratio (OR) comparing cancer survivors to the cancer-free general population reported in each study is presented numerically and graphically, with point estimates shown as boxes scaled by study weight and whiskers representing the 95% confidence intervals (CIs). The overall pooled OR, calculated using a random-effects model, is represented by a diamond, with its width corresponding to the 95% CI. Studies which assessed screening methods other than mammography were excluded.

Supplementary Figure 11. Forest plot of the odds ratio for breast cancer screening, comparing cancer survivors to the cancer-free general population. The odds ratio (OR) reported in each study is presented numerically and graphically, with point estimates shown as boxes scaled by study weight and whiskers representing the 95% confidence intervals (CIs). The overall pooled OR, calculated using a random-effects model, is represented by a diamond, with its width corresponding to the 95% CI.

Supplementary Figure 7. Pooled prevalence of breast cancer screening by time between cancer diagnosis and screening assessment. The pooled prevalence estimates of breast cancer screening, calculated using random-effects models, are displayed as diamonds with whiskers indicating 95% confidence intervals (CIs). Estimates were pooled separately based on time from cancer diagnosis to screening assessment, categorized as studies with mean/median time since diagnosis ≥5 years, <5 years, or including cancer survivors irrespective of and not reporting on time since diagnosis (i.e., ever diagnosed).The chi-square statistic was used to test for differences in pooled estimates across subgroups (p=0.206).

Supplementary Figure 2. Pooled prevalence of breast cancer screening by age eligibility and screening definition. The pooled prevalence estimates of breast cancer screening, calculated using random-effects models, are displayed as diamonds with whiskers indicating 95% confidence intervals (CIs). Estimates were pooled separately by study population (screening-eligible age only [≥40 years] vs. inclusion of non-eligible ages) and by screening definition (screened within a specified time period vs. ever screened) and the chi-square statistic used to test for differences in pooled estimates across subgroups (p=0.565).

Supplementary Figure 17. Pooled odds ratio of breast cancer screening comparing cancer survivors to the cancer-free general population, stratified by study quality. The odds ratio (OR) reported in each study is presented numerically and graphically, with point estimates shown as boxes scaled by study weight and whiskers representing the 95% confidence intervals (CIs). Estimates were pooled separately based on study quality (assessed as low, moderate or high). The chi-square statistic was used to test for differences in pooled estimates across subgroups (p=0.557). Pooled ORs, calculated using a random-effects model, are represented by diamonds with their width corresponding to the 95% CI.

Supplementary Figure 13. Sensitivity analysis for meta-analysis of the odds ratio for breast cancer screening comparing cancer survivors to the cancer-free general population excluding studies recruiting ever-screened and age-ineligible participants. The odds ratio (OR) comparing cancer survivors to the cancer-free general population reported in each study is presented numerically and graphically, with point estimates shown as boxes scaled by study weight and whiskers representing the 95% confidence intervals (CIs). The overall pooled OR calculated using a random-effects model, is represented by a diamond, with its width corresponding to the 95% CI. Studies which included age-ineligible participants (<40 years old), or which defined screening participation as ever being screened were excluded.

Supplementary Figure 21. Pooled odds ratio of breast cancer screening comparing cancer survivors to the cancer-free general population, stratified by primary cancer site. The odds ratio (OR) reported in each study is presented numerically and graphically, with point estimates shown as boxes scaled by study weight and whiskers representing the 95% confidence intervals (CIs). The OR among studies including survivors of mixed cancer sites (A), colorectal (B), blood (C), cervical (D), and uterine cancer (E) were pooled separately, with site-specific pooled estimates (represented by a diamond) presented in each panel.

Supplementary Table 2. Number of studies by the method of screening ascertainment according to primary cancer sites for breast cancer screening prevalence

Cancer survivors are at increased risk of developing subsequent primary cancers, yet participation in recommended cancer screening remains suboptimal. Greater understanding of factors impacting screening participation is needed to inform strategies for improving uptake. Therefore, we aimed to review and quantitatively synthesize predictors of participation in breast cancer (BC) and colorectal cancer (CRC) screening among cancer survivors. MEDLINE (Ovid), EMBASE, PubMed, and CINAHL databases were searched from inception through September 2024 to identify studies examining predictors of CRC or BC screening among survivors of other adult cancers. Random effect models were used to estimate pooled associations for predictors reported in ≥ 3 studies. From 2492 initial citations, 49 studies were included, with 35 studies reporting on CRC and 29 on BC screening participation. Factors significantly associated with CRC screening included having a healthcare provider, having health insurance, receiving a provider recommendation for screening, a greater number of physician visits, older age, being a nonsmoker, being married, higher income or education, urban residence, and better mental health. Factors associated with BC screening included receiving a written follow-up care plan, a greater number of physician visits, being married, not having dual Medicaid-Medicare coverage status, receiving specialist care, and White racial identity. Healthcare access and survivorship care factors appear to play an important role in screening participation among cancer survivors. Interventions addressing barriers to care and social determinants of health may help improve screening uptake and reduce disparities in subsequent cancer screening in this high-risk population.

PurposeIn radiation oncology (RO), peer review (PR) rounds are essential for ensuring quality care, enhancing team communication, and identifying areas for improvement in radiotherapy (RT) plans. However, time constraints, lengthy discussions, and imbalanced team contributions often hinder effective PR. This scoping review examined novel tools and processes to enhance PR efficiency and experience in modern academic centers.Materials and methodsWe queried six databases [MEDLINE (Ovid), EMBASE, PubMed, Cochrane Library, CINAHL, and MEDLINE (Ebsco)] and the gray literature, yielding 8,955 citations. Studies were excluded if they (1) were focused on comparisons involving paper-based rounds, (2) lacked clear relevance to PR processes in RT, or (3) did not explicitly address efficiency within PR activities.ResultsTwelve studies focusing on PR structure and efficiency-related processes were included. Of the identified, 11/12 explored various structural formats to improve facilitation, 5/12 discussed automated tools, and 2/12 evaluated checklists. Only half of studies reported a PR-associated time burden, with 2/12 reporting positive post-implementation changes. The remaining studies did not measure comparative times.ConclusionsThis scoping review reveals the lack of work on innovative approaches to optimize PR rounds in RO, despite the commonly reported participation barrier of high time commitment. Our findings highlight the importance of integrating automation in order to streamline facilitation methods and tools such as checklists to reduce inefficiency, given PR’s essential role in patient safety and clinical learning. Future research should prioritize the development and evaluation of time-saving strategies and tools for PR in RO workflow to optimize its sustainability and impact.

Supplementary Table 1. Database search strategy for this systematic review.

Abstract Background Endoscopic retrograde cholangiopancreatography (ERCP) is a ubiquitous and high-risk procedure with variable performance across providers and centres. In this review, we aimed to summarize the evidence related to ERCP practice in 4 domains: indications and alternatives, quality indicators, training and credentialing, and facility standards. Methods We searched MEDLINE, Embase, Cochrane databases, and the grey literature (2000-2026) for ERCP quality standards published by professional societies or health agencies. Records were included if they included recommendations related to the 4 above-mentioned domains. Recommendations were described qualitatively. For quality indicators, we described benchmarks for performance, strength of recommendations, and quality of evidence when available. Results Fifty-seven reports were included from 4 continents. Eighteen unique indications were identified. Seventeen procedural quality indicators were identified. Priority indicators included appropriate indication (benchmark >90% of cases), cannulation success rate (≥85%-90%), management of common bile duct stones <1 cm (≥75%-90%) stent placement below the bifurcation (≥80%-95%), post-ERCP pancreatitis rate (≤6%-10%), and unplanned hospital visit within 30 days of ERCP (<15%). Training recommendations suggested minimum volumes of 100-300 supervised procedures and highlighted a shift towards competency-based assessment tools like The EUS and ERCP Skills Assessment Tool or Direct Observation of Procedural Skills. Facility standards focused on radiation safety, duodenoscope reprocessing/infection control, and mandatory photodocumentation. Conclusion There was substantial alignment among societies on several core ERCP quality metrics, yet variability remains in training and credentialing and facility standards. Significant gaps exist regarding maintenance of competence and ERCP assistant training. This evidence can support health authorities seeking to develop and implement ERCP quality improvement initiatives.

Supplementary Figure 9. Pooled prevalence of breast cancer screening by geographical region. The pooled prevalence estimates of breast cancer screening, calculated using random-effects models, are displayed as diamonds with whiskers indicating 95% confidence intervals (CIs). Estimates were pooled separately based on geographical region, categorized as studies conducted in North America or other, based on data availability. The chi-square statistic was used to test for differences in pooled estimates across subgroups (p=0.026).