摘要
一位新闻记者-机器人与机器学习的工作人员新闻编辑每日新闻-肿瘤学的新研究-神经内分泌癌是一篇报道的主题。根据来自英国伦敦的新闻，NewsRx记者报道，研究表明：“神经内分泌肿瘤(NETs)的发病率正在增加，通常在晚期诊断，个体可能会经历多年的诊断延迟，特别是当发生于小肠(SI)时。临床预测模型可以为初级保健中的病例发现提供新的机会。”我们的新闻记者从研究中获得了一句话：“确定了一个开放的ADUL TS队列（18岁以上），为最佳患者护理研究数据库B etween 2000年1月1日和2023年3月30日提供数据。该数据库收集了来自英国全科医生的D E识别数据。模型开发方法包括逻辑回归、惩罚回归、使用内部-外部交叉有效性评估性能（判别和校准）。决策分析曲线比较临床效用。在1170万名个体中，382名记录了SI净诊断（0.003%）。XGBoost模型EL具有最高的AUC（0.869，95%置信区间[CI]：0.841-0.898），但轻度校准错误（斜率1.165，95%CI：1.088-1.243；整体校准0.010，95%可信区间:-0.164~0.185)。所有模型的临床效用相似。多变量预测模型在利用初级保健记录中的信息识别未诊断的SI NET患者方面可能具有临床效用。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Neuroendocr ine Cancer is the subject of a report. According to news originating from London , United Kingdom, by NewsRx correspondents, research stated, "Neuroendocrine tum ours (NETs) are increasing in incidence, often diagnosed at advanced stages, and individuals may experience years of diagnostic delay, particularly when arising from the small intestine (SI). Clinical prediction models could present novel o pportunities for case finding in primary care." Our news journalists obtained a quote from the research, "An open cohort of adul ts (18+ years) contributing data to the Optimum Patient Care Research Database b etween 1st Jan 2000 and 30th March 2023 was identified. This database collects d e-identified data from general practices in the UK. Model development approaches comprised logistic regression, penalised regression, and XGBoost. Performance ( discrimination and calibration) was assessed using internal-external cross-valid ation. Decision analysis curves compared clinical utility. Of 11.7 million indiv iduals, 382 had recorded SI NET diagnoses (0.003 %). The XGBoost mod el had the highest AUC (0.869, 95% confidence interval [CI]: 0.841-0.898) but was mildly miscalibrated (slope 1.165, 95% CI: 1.088-1.243; calibration-in-the-large 0.010, 95% CI: -0.164 to 0.185). Clinical utility was similar across all models. Multivaria ble prediction models may have clinical utility in identifying individuals with undiagnosed SI NETs using information in their primary care records."
NETL
NSTL