Trajectories of childhood anxiety disorders in two generations at high risk
Bibliographic record
Abstract
BACKGROUND: The course of anxiety disorders during childhood is heterogeneous. In two generations at high or low risk, we described the course of childhood anxiety disorders and evaluated whether parent or grandparent major depressive disorder (MDD) predicted a persistent anxiety course. METHODS: We utilized a multigenerational study (1982-2015), following children (second generation, G2) and grandchildren (third generation, G3) of generation 1 (G1) with either moderate/severe MDD or no psychiatric illness. Psychiatric diagnoses were based on diagnostic interviews. Using group-based trajectory models, we identified clusters of children with similar anxiety disorder trajectories (age 0-17). RESULTS: We identified three primary trajectories in G2 (N = 275) and G3 (N = 118) cohorts: "no/low anxiety disorder" during childhood (G2 = 66%; G3 = 53%), "nonpersistent" with anxiety during part of childhood (G2 = 16%; G3 = 21%), and "persistent" (G2 = 18%; G3 = 25%). Childhood mood disorders and substance use disorders tended to be more prevalent in children in the persistent anxiety trajectory. In G2 children, parent MDD was associated with an increased likelihood of being in the persistent (84%) or nonpersistent trajectory (82%) versus no/low anxiety trajectory (62%). In G3 children, grandparent MDD, but not parent, was associated with an increased likelihood of being in the persistent (83%) versus nonpersistent (48%) and no/low anxiety (51%) trajectories. CONCLUSION: Anxiety trajectories move beyond what is captured under binary, single time-point measures. Parent or grandparent history of moderate/severe MDD may offer value in predicting child anxiety disorder course, which could help clinicians and caregivers identify children needing increased attention and screening for other psychiatric conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".