37 Clinical utility of the BEARS as a sensitive screener for sleep problems in ADHD.
Bibliographic record
Abstract
Objective: Many children and adolescents do not achieve adequate sleep durations. The prevalence of sleep problems has been estimated at 7% for typically developing children (Corkum, Tannock, & Moldofsky, 1998) and as high as 45% for representative samples of children, including participants with various diagnoses in proportion to what would be expected in the population (Sher-Fen Gau, 2006). For children with ADHD, the prevalence of sleep problems has been estimated at between 25-50% (Corkum, Tannock, & Moldofsky, 1998). Given the important role that sleep plays in children with ADHD, a brief and effective screener is needed to aid clinicians in assessing for sleep problems, especially when the referral for a neuropsychological evaluation concerns ADHD or any other neurodevelopmental disorder for which presenting concerns involve symptoms that overlap with ADHD. While the developers of the BEARS have demonstrated its utility as a screening tool, there is currently no independent published research replicating this finding. The current study aimed to replicate the findings of the BEARS developers by demonstrating its utility as a sensitive screening tool for sleep problems. It was predicted that the BEARS would demonstrate high sensitivity in identifying children with sleep problems. Participants and Methods: Data from 54 school aged children (aged 6-147-13, Mage = 9.83) was analysed. Children were administered the BEARS, and caregivers completed the BEARS and Children's Sleep Habits Questionnaire (CSHQ), as part of a larger study. Results: Binomial logistic regression model was statistically significant, x2(2) = 20.508, p < .0005. The model explained 46.8% (Nagelkerke R2) of the variance and correctly classified 70.8% of cases. Sensitivity was 78.6%, specificity was 60.0%, positive predictive value was 73.3%, and negative predictive value was 66.7%. Both predictor variables, parent reported BEARS (p = .001) and child-reported BEARS (p = .049), were significant. Children with higher BEARS parent report scores had 3.27 times higher odds, and those with higher self-report scores had 2.88 times higher odds, of exceeding the CSHQ cut-off than those with lower scores. ROC curve analysis revealed that the BEARS parent and self-report scores had excellent diagnostic utility (Hosmer et al., 2013) for accurately classifying children who exceeded the cut-off on the CSHQ from those who did not (area under the curve [AUC] = 0.849, SE = 0.054, 95% CI = .742 to .956, p < .001). Conclusions: The results of the current study indicate that the BEARS has excellent diagnostic utility for accurately classifying sleep problems. Additionally, it is quick to administer making it a practical screening tool for clinicians to include as part of a comprehensive neuropsychological assessment.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".