RESUMO
Objective: Despite expanded guidelines, adolescent gonorrhea and chlamydia (GC/CT) screening rates remain low due to multiple psychosocial barriers and biases. This intervention aimed to improve screening and diagnosis rates at adolescent well visits by establishing a streamlined universal screening protocol for all patients ages 13-18 years old. Methods: A universal sexually transmitted infection (STI) screening approach was introduced at an urban clinic affiliated with an academic medical center near Philadelphia, Pennsylvania (PA) in September 2018 for all adolescent well-visits. GC/CT screening and diagnosis rates were compared two years prior to and two years after implementation, deemed the baseline and intervention groups, respectively. Results: In total, 1,168 encounters were included for analysis. The patient cohort consisted of 47% females, with an average age of 15, and were predominantly publicly insured (79%). STI screening rates increased significantly from 16.7% (89/534) to 83.6% (530/634) of adolescents with implementation of the universal screening protocol. Furthermore, there was a 1.6-fold increase in total positive cases detected after implementation of ok universal screening. Conclusion: This study demonstrates improved adolescent GC/CT capture rates by establishing a universal screening protocol and highlights a streamlined means of implementation in virtually any pediatric clinic. Limitations include sample size, as this is a single academic practice, as well as any issues with lab collection and results reporting.
RESUMO
Background/objective: Pain is a challenging multifaceted symptom reported by most cancer patients, resulting in a substantial burden on both patients and healthcare systems. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and supporting decision-making processes in pain management in cancer. Methods: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms including "Cancer", "Pain", "Pain Management", "Analgesics", "Opioids", "Artificial Intelligence", "Machine Learning", "Deep Learning", and "Neural Networks" published up to September 7, 2023. The screening process was performed using the Covidence screening tool. Only original studies conducted in human cohorts were included. AI/ML models, their validation and performance and adherence to TRIPOD guidelines were summarized from the final included studies. Results: This systematic review included 44 studies from 2006-2023. Most studies were prospective and uni-institutional. There was an increase in the trend of AI/ML studies in cancer pain in the last 4 years. Nineteen studies used AI/ML for classifying cancer patients' pain development after cancer therapy, with median AUC 0.80 (range 0.76-0.94). Eighteen studies focused on cancer pain research with median AUC 0.86 (range 0.50-0.99), and 7 focused on applying AI/ML for cancer pain management decisions with median AUC 0.71 (range 0.47-0.89). Multiple ML models were investigated with. median AUC across all models in all studies (0.77). Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence of included studies to TRIPOD guidelines was 70.7%. Lack of external validation (14%) and clinical application (23%) of most included studies was detected. Reporting of model calibration was also missing in the majority of studies (5%). Conclusion: Implementation of various novel AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. These advanced tools will integrate big health-related data for personalized pain management in cancer patients. Further research focusing on model calibration and rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.
