Adenovirus-Associated Virus Vector-Mediated Gene Transfer in Hemophilia B

NIHR (RP-PG-0310-1001), the Medical Research Council, the Katharine Dormandy Trust, the U.K. Department of Health, NHS Blood and Transplant, the NIHR Biomedical Research Centers (to University College London Hospital and University College London), the ASSISI Foundation of Memphis, the American Lebanese Syrian Associated Charities, the Howard Hughes Medical Institute, the National Heart, Lung, and Blood Institute (HL094396), the Royal Free Hospital Charity Special Trustees Fund 35, the Royal Free Hospital NHS Trust, and St. Jude Children’s Research Hospita

Decreased fertility among female childhood cancer survivors who received 22-27 Gy hypothalamic/pituitary irradiation: A report from the Childhood Cancer Survivor Study

Objective: To evaluate the effect of hypothalamic/pituitary radiation (HPT RT) dose on the occurrence of first pregnancy. Design: Retrospective cohort study of childhood cancer 5-year survivors (CCS) diagnosed between 1970 and 1986 before 21 years of age at one of 26 North American pediatric cancer treatment centers. Setting: Self-administered questionnaire. Patient(s): A total of 3,619 female CCS who participated in the Childhood Cancer Survivor Study and received no or scatter (≤0.1 Gy) radiation to the ovaries and 2,081 female siblings (Sibs) of the participants. Intervention(s): None. Main Outcome Measure(s): Self-reported pregnancy events. Result(s): As a group, CCS were as likely to report being pregnant as Sibs (hazard ratio 1.07, 95% confidence interval 0.97-1.19). Multivariable models showed a significant decrease in the risk of pregnancy with HPT RT doses ≥22 Gy compared with those CCS receiving no HPT RT. Conclusion(s): These results support the hypothesis that exposures of 22-27 Gy HPT RT may be a contributing factor to infertility among female CCS. © 2011 by American Society for Reproductive Medicine

Natural Language Processing and Machine Learning Methods to Characterize Unstructured Patient-Reported Outcomes: Validation Study

BackgroundAssessing patient-reported outcomes (PROs) through interviews or conversations during clinical encounters provides insightful information about survivorship. ObjectiveThis study aims to test the validity of natural language processing (NLP) and machine learning (ML) algorithms in identifying different attributes of pain interference and fatigue symptoms experienced by child and adolescent survivors of cancer versus the judgment by PRO content experts as the gold standard to validate NLP/ML algorithms. MethodsThis cross-sectional study focused on child and adolescent survivors of cancer, aged 8 to 17 years, and caregivers, from whom 391 meaning units in the pain interference domain and 423 in the fatigue domain were generated for analyses. Data were collected from the After Completion of Therapy Clinic at St. Jude Children’s Research Hospital. Experienced pain interference and fatigue symptoms were reported through in-depth interviews. After verbatim transcription, analyzable sentences (ie, meaning units) were semantically labeled by 2 content experts for each attribute (physical, cognitive, social, or unclassified). Two NLP/ML methods were used to extract and validate the semantic features: bidirectional encoder representations from transformers (BERT) and Word2vec plus one of the ML methods, the support vector machine or extreme gradient boosting. Receiver operating characteristic and precision-recall curves were used to evaluate the accuracy and validity of the NLP/ML methods. ResultsCompared with Word2vec/support vector machine and Word2vec/extreme gradient boosting, BERT demonstrated higher accuracy in both symptom domains, with 0.931 (95% CI 0.905-0.957) and 0.916 (95% CI 0.887-0.941) for problems with cognitive and social attributes on pain interference, respectively, and 0.929 (95% CI 0.903-0.953) and 0.917 (95% CI 0.891-0.943) for problems with cognitive and social attributes on fatigue, respectively. In addition, BERT yielded superior areas under the receiver operating characteristic curve for cognitive attributes on pain interference and fatigue domains (0.923, 95% CI 0.879-0.997; 0.948, 95% CI 0.922-0.979) and superior areas under the precision-recall curve for cognitive attributes on pain interference and fatigue domains (0.818, 95% CI 0.735-0.917; 0.855, 95% CI 0.791-0.930). ConclusionsThe BERT method performed better than the other methods. As an alternative to using standard PRO surveys, collecting unstructured PROs via interviews or conversations during clinical encounters and applying NLP/ML methods can facilitate PRO assessment in child and adolescent cancer survivors