High separation anxiety trajectory in early childhood is a risk factor for sleep bruxism at age 7
Bibliographic record
Abstract
STUDY OBJECTIVES: The evolution of sleep bruxism manifestations and their co-occurrence with separation anxiety in early childhood remain unclear. Our threefold aim was to: (1) describe developmental sleep bruxism trajectories in early childhood, (2) investigate co-occurrences between trajectories of sleep bruxism and separation anxiety, and (3) determine whether distinct trajectories of separation anxiety increase the risk of presenting sleep bruxism during the first year of elementary school. METHODS: This study is part of the Québec Longitudinal Study of Child Development. Sleep bruxism scores were assessed from age 1.5 to 7 years with the Self-Administered Questionnaire for Mother (n = 1946). Separation anxiety scores were measured from age 1.5 to 6 years with the Interviewer-Completed Computerized Questionnaire (n = 2045). RESULTS: We identified four sleep bruxism trajectories from age 1.5 to 6 years: High-Increasing sleep bruxism at age 1.5 (14.1%), High-Increasing sleep bruxism at age 4 (18.3%), Low-Persistent sleep bruxism (12.1%), and Never-Persistent sleep bruxism (55.5%); and four separation anxiety trajectories from age 1.5 to 6 years: Low-Persistent separation anxiety (60.2%), High-Increasing separation anxiety (6.9%), High-Decreasing separation anxiety (10.8%), and Low-Increasing separation anxiety (22.1%). Sleep bruxism and separation anxiety trajectories were weakly associated (X2 = 37.84, p < 0.001). Compared with preschoolers belonging to the Low-Persistent separation anxiety trajectory, preschoolers in the High-Increasing separation anxiety trajectory had almost double the risk of presenting sleep bruxism at age 7 (95% CI = 1.25-3.22, p = 0.04). CONCLUSION: When separation anxiety issues are detected in early childhood, it would be useful to target sleep bruxism during the first year of elementary school.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".