Distinct Long-term OCD Symptom Severity Trajectories, Related Predictors, and Quality of Life over Three Years
Bibliographic record
Abstract
Objectives The aim of the present study was to investigate long-term OCD symptom severity trajectories in the NordLOTS sample during and up to three years after stepped-care treatment and to detect predictors of trajectory group membership. Further, the aim was to investigate quality of life over three years in the trajectory groups. Methods Long-term symptom severity trajectories were investigated using latent class growth analysis (LCGA) on data from all 269 patients from the NordLOTS who were assessed at seven time points over three years: Pre-CBT, mid-treatment, post-CBT and 6, 12, 24, and 36 months after treatment. Symptom severity was assessed using the Children's Yale-Brown Obsessive-Compulsive Scale (CY-BOCS). Predictors of class membership were investigated using multivariate analysis. Patient and parent proxy ratings of quality of life were assessed at the same seven time points (n = 220). Results Three distinct long-term OCD symptom severity trajectory groups were identified: a) acute, sustained responders (54.6%); b) slow, continued responders (23.4%); and c) limited long-term responders (21.9%). Baseline predictors of group membership pertained to age, symptom severity, contamination/cleaning, and anxiety symptoms. Further, the groups showed differences in quality of life over the three years compared to norm levels. Conclusions Clinical attention is required for adolescent OCD patients showing less convincing response to first-line CBT as well as contamination/cleaning and anxiety symptoms. They may have reached the established clinician-rated cut-off for treatment response, yet patient-rated quality of life assessment after treatment could detect patients in need of further care.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".