Development and Validation of SDBeasy Score as a Predictor of Behavioral Outcomes in Childhood
Bibliographic record
Abstract
Abstract Rationale There are limited tools to identify which children are at greatest risk for developing sleep-disordered breathing (SDB)-associated behavioral morbidity. Objectives To examine associations between age of onset and duration of parent-reported symptoms of SDB and behavioral problems at the age of 5 years. Methods Data were collected and analyses were completed for participants in the CHILD (Canadian Healthy Infant Longitudinal Development) cohort at the Edmonton and Toronto sites. We generated an SDBeasy score on the basis of the age of onset and duration of SDB symptoms as reported by parents completing the Pediatric Sleep Questionnaire. Using CHILD-Edmonton data, we completed multivariate linear regression to determine whether the SDBeasy score was associated with behavioral problems at the age 5 years of age as assessed by using the Child Behavior Checklist (CBCL). We then validated the SDBeasy score using CHILD-Toronto data. Measurements and Main Results At the 5-year visit, 581 of 716 (81%) CHILD-Edmonton participants still enrolled had CBCL data. Of the 581 children with data, 77% (446 of 581) had an SDBeasy score of 0 (never had SDB symptoms), whereas 20 of 581 children (3.4%) had persistent SDB symptoms from infancy through 5 years of age (SDBeasy score of 24). Children had a 0.35-point–higher CBCL total behavioral score at 5 years for each 1-point increase in their SDBeasy score (95% confidence interval, 0.24–0. 5; P < 0.01). We found consistent results among CHILD-Toronto participants; children had a 0.26-point–higher CBCL total behavioral score at 5 years for each 1-point increase in their SDBeasy score (95% confidence interval, 0.08–0.44; P = 0.005). Conclusions The SDBeasy score, based on the Pediatric Sleep Questionnaire, enables identification of children with higher behavioral-problem scores.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".