PURPOSE Patients undergoing outpatient radiotherapy (RT) or chemoradiation (CRT) frequently require acute care (emergency department evaluation or hospitalization). Machine learning (ML) may guide interventions to reduce this risk. There are limited prospective studies investigating the clinical impact of ML in health care. The objective of this study was to determine whether ML can identify high-risk patients and direct mandatory twice-weekly clinical evaluation to reduce acute care visits during treatment. PATIENTS AND METHODS During this single-institution randomized quality improvement study (ClinicalTrials.gov identifier: NCT04277650 ), 963 outpatient adult courses of RT and CRT started from January 7 to June 30, 2019, were evaluated by an ML algorithm. Among these, 311 courses identified by ML as high risk (> 10% risk of acute care during treatment) were randomized to standard once-weekly clinical evaluation (n = 157) or mandatory twice-weekly evaluation (n = 154). Both arms allowed additional evaluations on the basis of clinician discretion. The primary end point was the rate of acute care visits during RT. Model performance was evaluated using receiver operating characteristic area under the curve (AUC) and decile calibration plots. RESULTS Twice-weekly evaluation reduced rates of acute care during treatment from 22.3% to 12.3% (difference, −10.0%; 95% CI, −18.3 to −1.6; relative risk, 0.556; 95% CI, 0.332 to 0.924; P = .02). Low-risk patients had a 2.7% acute care rate. Model discrimination was good in high- and low-risk patients undergoing standard once-weekly evaluation (AUC, 0.851). CONCLUSION In this prospective randomized study, ML accurately triaged patients undergoing RT and CRT, directing clinical management with reduced acute care rates versus standard of care. This prospective study demonstrates the potential benefit of ML in health care and offers opportunities to enhance care quality and reduce health care costs.

BACKGROUND: Investigating scientific publication trends in the field of oncology may highlight opportunities for improved representation, mentorship, collaboration, and advancement for women. METHODS: We conducted a bibliometric analysis of 2017. Full name and degree credentials per author role (ie, first or senior author), article type, publication year, and citation metrics were collected. First names were used to identify author gender. RESULTS: Across 9189 articles, female representation rose between 1990 and 2017 (first authors: 17. 7% in 1990, 36.6% in 2017; senior authors: 11.7% in 1990, 28.5% in 2017). For the 50 most cited articles per year, women comprised a smaller percent of first (26.5%) and senior (19.9%) authors. The average citation count was higher for male first (44.8 per article) and senior (47.1) authors compared to female first (39.7) and senior (44.1) authors. With male senior authors, the first author was more likely male (71.4% male; 25.0% female); with female senior authors, first authors were 50.2% male and 47.6% female. IJROBP had the lowest total female representation among first (25.1%) and senior (16.7%) authors. Women had more MDs with Masters degrees, whereas men held more MDs only and more MDs with PhDs. CONCLUSION: Despite positive trends, substantial gendered differences in oncology publications persist. Fostering more women in oncology research will benefit female representation at many levels of academia and improve productivity, collaboration, and recruitment, especially in technical fields such as radiation and surgical oncology. Cancer 2020;126:2859-2865.

PURPOSE Mortality for patients with classical Hodgkin lymphoma (cHL) treated during an era characterized in the United States by widespread use of doxorubicin, bleomycin, vinblastine, and dacarbazine and diminishing use of radiotherapy is not well understood. PATIENTS AND METHODS We identified 20,007 individuals diagnosed with stage I/II (early) or III/IV (advanced) cHL between age 20 and 74 years treated with initial chemotherapy in US population-based cancer registries during 2000-2015 (follow-up through 2016). We used standardized mortality ratios (SMRs) to compare cause-specific relative mortality risk following cHL to that expected in the general population and estimated excess absolute risks (EARs; per 10,000 patient-years) to quantify disease-specific death burden. RESULTS We identified 3,380 deaths in the cHL cohort, including 1,321 (39%) not attributed to lymphoma. Overall, noncancer SMRs were increased 2.4-fold (95% CI, 2.2 to 2.6; observed, 559; EAR, 61.6) and 1.6-fold (95% CI, 1.4 to 1.7; observed, 473; EAR, 18.2) for advanced- and early-stage cHL, respectively, compared with the general US population. SMRs and EARs differed substantially by cause of death and cHL stage. Among the highest EARs for noncancer causes of death were those for heart disease (EAR, 15.1; SMR, 2.1), infections (EAR, 10.6; SMR, 3.9), interstitial lung disease (ILD; EAR, 9.7; SMR, 22.1), and adverse events (AEs) related to medications/drugs (EAR, 7.4; SMR, 5.0) after advanced-stage cHL and heart disease (EAR, 6.6; SMR, 1.7), ILD (EAR, 3.7; SMR, 13.1), and infections (EAR, 3.1; SMR, 2.2) after early-stage cHL. Strikingly elevated SMRs for ILD, infections, and AEs were observed < 1 year after cHL. Individuals age 60-74 years with advanced-stage cHL experienced a disproportionate excess of deaths as a result of heart disease, ILD, infections, AEs, and solid tumors. CONCLUSION Despite evolving cHL treatment approaches, patients continue to face increased nonlymphoma mortality risks from multiple, potentially preventable causes. Surveillance, early interventions, and cHL treatment refinements may favorably affect patient longevity, particularly among high-risk subgroups.