Fletcher Stroud (sliprefund4)

learning content through practice constitutes the key element for transfer into long-term memory. This study demonstrated significant learning success for both groups in their own self-assessment as well as in the results of the practical exercises. Subsequently, the superiority of the PDCA cycle could be shown for almost all criteria for surgical suturing techniques. Several studies prioritize the teaching of practical skills according to Peyton and consider step 3 ("comprehension") to be the essential factor. The PDCA cycle, which has its origins in industrial quality management, and its success can be understood from the perspective of learning theory in terms of Jean Piaget's model of equilibration. The necessity of active reflection on the learning content through practice constitutes the key element for transfer into long-term memory. Age and time information stored within the histories of clinical notes can provide valuable insights for assessing a patient's disease risk, understanding disease progression, and studying therapeutic outcomes. However, details of age and temporally-specified clinical events are not well captured, consistently codified, and readily available to research databases for study. We expanded upon existing annotation schemes to capture additional age and temporal information, conducted an annotation study to validate our expanded schema, and developed a prototypical, rule-based Named Entity Recognizer to extract our novel clinical named entities (NE). The annotation study was conducted on 138 discharge summaries from the pre-annotated 2014 ShARe/CLEF eHealth Challenge corpus. In addition to existing NE classes (TIMEX3, SUBJECT_CLASS, DISEASE_DISORDER), our schema proposes 3 additional NEs (AGE, PROCEDURE, OTHER_EVENTS). We also propose new attributes, e.g., "degree_relation" which captures the degree of biological relation for subjects annotated under SUBJECT_CLASS. As a proof of concept, we applied the schema to 49 H&P notes to encode pertinent history information for a lung cancer cohort study. An abundance of information was captured under the new OTHER_EVENTS, PROCEDURE and AGE classes, with 23%, 10% and 8% of all annotated NEs belonging to the above classes, respectively. We observed high inter-annotator agreement of >80% for AGE and TIMEX3; the automated NLP system achieved F1 scores of 86% (AGE) and 86% (TIMEX3). Age and temporally-specified mentions within past medical, family, surgical, and social histories were common in our lung cancer data set; annotation is ongoing to support this translational research study. Our annotation schema and NLP system can encode historical events from clinical notes to support clinical and translational research studies. Our annotation schema and NLP system can encode historical events from clinical notes to support clinical and translational research studies. Although a U-shaped association between sleep duration and all-cause mortality has been found in general population, its association in the elderly adults, especially in the oldest-old, is rarely explored. In present cohort study, we prospectively explore the association between sleep duration and all-cause mortality among 15,092 participants enrolled in the Chinese Longitudinal Healthy Longevity Survey (CLHLS) from 2005 to 2019. Sleep duration and death information was collected by using structured questionnaires. Cox regression model with sleep duration as a time-varying exposure was performed to calculate the hazard ratios (HRs) and 95% confidence intervals (CIs). The dose-response association between them was explored via a restricted cubic spline function. During an average follow-up of 4.51 (standard deviation, SD 3.62) years, 10,768 participants died during the follow-up period. The mean (SD) age of the participants was 89.26 (11.56) years old. Compared to individuals with moderate sleep duration (7-8 ho