0212 Associations Between Sleep EEG and Report Card Grades in Mathematics in Adolescents
Bibliographic record
Abstract
Abstract Introduction Academic success plays an important role in improving future lifetime opportunities. Numerous factors have been identified as being relevant to academic achievement, but the role played by sleep in this process has been largely under studied. Accumulating evidence indicates that healthy sleep has beneficial effects on academic success, but the brain mechanisms underlying the interplay between sleep and academic outcomes in adolescents are poorly understood. Sleep electroencephalogram (EEG) is tightly linked to structural and functional features of the central nervous system. EEG signals during NREM and REM sleep have been validated to be excellent noninvasive correlates of adolescents’ brain maturation. However, previous studies examining the associations between sleep and academic achievement were based on primarily subjective measures of sleep or actigraphy. Thus, it is not known if or how these associations are related to adolescents’ brain activity during the night. The objective of this study was to examine the association between sleep EEG and report card marks in Mathematics in healthy adolescents. Methods Sample of 40 adolescents (26 girls, 18 boys) between the ages of 12 to 15 years (M= 13.9; SD = 0.95) participated in the study. Sleep EEG was recorded using a single night of ambulatory sleep EEG monitoring with frontal derivations during the school week in the child’s home. The Sleep Profiler was used to evaluate sleep EEG through the measurement of ambulatory EEG recorded from three frontal sensors placed at approximately F7, F8 and Fpz; Automated sleep/wake scoring was performed using the Sleep Profiler system followed by visual analysis by an experienced pediatric sleep specialist. Report card grades were used to assess adolescents’ academic achievement in Mathematics for the current semester. Results Multiple linear regression analyses revealed that longer duration of N3 sleep was significantly associated with higher marks in Mathematics, above and beyond the contributions of age, gender, socioeconomic status and PSG-measured sleep duration. Conclusion Our findings suggest that longer duration of N3 sleep recorded in the home environment in healthy adolescents is positively associated with better grades in Mathematics. Support (if any) The Azrieli Centre for Autism Research NSERC Discovery Grant RGPIN-2021-03363 Reut Gruber
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".