Dataset Information

Morning brain: Real-world neural evidence that high school class times matter.

ABSTRACT: Researchers, parents, and educators consistently observe a stark mismatch between biologically preferred and socially imposed sleep-wake hours in adolescents, fueling debate about high school start times. We contribute neural evidence to this debate with electroencephalogram (EEG) data collected from high school students during their regular morning, mid-morning and afternoon classes. Overall, student alpha power was lower when class content was taught via videos than through lectures. Students' resting state alpha brain activity decreased as the day progressed, consistent with adolescents being least attentive early in the morning. During the lessons, students showed consistently worse performance and higher alpha power for early morning classes than for mid-morning classes, while afternoon quiz scores and alpha levels varied. Together, our findings demonstrate that both class activity and class time are reflected in adolescents' brain states in a real-world setting, and corroborate educational research suggesting that mid-morning may be the best time to learn.

SUBMITTER: Dikker S

PROVIDER: S-EPMC7745151 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Morning brain: real-world neural evidence that high school class times matter.

Dikker Suzanne S Haegens Saskia S Bevilacqua Dana D Davidesco Ido I Wan Lu L Kaggen Lisa L McClintock James J Chaloner Kim K Ding Mingzhou M West Tessa T Poeppel David D

Social cognitive and affective neuroscience 20201201 11

Researchers, parents and educators consistently observe a stark mismatch between biologically preferred and socially imposed sleep-wake hours in adolescents, fueling debate about high school start times. We contribute neural evidence to this debate with electroencephalogram data collected from high school students during their regular morning, mid-morning and afternoon classes. Overall, student alpha power was lower when class content was taught via videos than through lectures. Students' restin ...[more]

PMID: 33068110

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Project description:Introduction Real-world evidence is important in regulatory and funding decisions. Manual data extraction from electronic health records (EHRs) is time-consuming and challenging to maintain. Automated extraction using natural language processing (NLP) and artificial intelligence may facilitate this process. Whereas NLP offers a faster solution than manual methods of extraction, the validity of extracted data remains in question. The current study compared manual and automated data extraction from the EHR of patients with advanced lung cancer. Methods Previously, we extracted EHRs from 1209 patients diagnosed with advanced lung cancer (stage IIIB or IV) between January 2015 and December 2017 at Princess Margaret Cancer Centre (Toronto, Canada) using the commercially available artificial intelligence engine, DARWEN (Pentavere, Ontario, Canada). For comparison, 100 of 333 patients that received systemic therapy were randomly selected and clinical data manually extracted by two trained abstractors using the same accepted gold standard feature definitions, including patient, disease characteristics, and treatment data. All cases were re-reviewed by an expert adjudicator. Accuracy and concordance between automated and manual methods are reported. Results Automated extraction required considerably less time (<1 day) than manual extraction (∼225 person-hr). The collection of demographic data (age, sex, diagnosis) was highly accurate and concordant with both methods (96%–100%). Accuracy (for either extraction approach) and concordance were lower for unstructured data elements in EHR, such as performance status, date of diagnosis, and smoking status (NLP accuracy: 88%–94%; Manual accuracy: 78%–94%; concordance: 71%–82%). Concurrent medications (86%–100%) and comorbid conditions (96%–100%), were reported with high accuracy and concordance. Treatment details were also accurately captured with both methods (84%–100%) and highly concordant (83%–99%). Detection of whether biomarker testing was performed was highly accurate and concordant (96%–98%), although detection of biomarker test results was more variable (accuracy 84%–100%, concordance 84%–99%). Features with syntactic or semantic variation requiring clinical interpretation were extracted with slightly lower accuracy by both NLP and manual review. For example, metastatic sites were more accurately identified through NLP extraction (NLP: 88%–99%; manual: 71%–100%; concordance: 70%–99%) with the exception of lung and lymph node metastases (NLP: 66%–71%; manual: 87%–92%; concordance: 58%) owing to analogous terms used in radiology reports not being included in the accepted gold standard definition. Conclusions Automated data abstraction from EHR is highly accurate and faster than manual abstraction. Key challenges include poorly structured EHR and the use of analogous terms beyond the accepted gold standard definition. The application of NLP can facilitate real-world evidence studies at a greater scale than could be achieved with manual data extraction

Dataset Information

Morning brain: Real-world neural evidence that high school class times matter.

Publications

Morning brain: real-world neural evidence that high school class times matter.

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