Obsessive-Compulsive Disorder in Children and Adolescents: Results from the Nordic Long-Term OCD Treatment Study (NordLOTS)
Bibliographic record
Abstract
Objectives Obsessive-Compulsive Disorder (OCD) affects 1-2% of the child and adolescent population, severely affecting their quality of life. Cognitive-behavioral therapy (CBT) and medication are efficacious, but a significant proportion of patients still fail to respond adequately to treatment. Few studies have systematically looked at the long-term outcomes, specific group outcome trajectories, and predictors of outcomes. The overall aim is to use the study results to guide practitioners with patients who do not respond to CBT. Methods Six presentations are based on results from the Nordic Long-term OCD Treatment Study (NordLOTS) that included 269 OCD patients aged 7-17 years from Denmark, Norway and Sweden. All participants received stepped-care treatment starting with 14 weekly sessions of CBT. Non-responders were randomized to either prolonged CBT or SSRIs. All patients were monitored for three years after ended CBT. Results Long-term treatment effects for pediatric OCD were sustainable for the sample as a whole, but subgroups with less favorable outcome trajectories were identified based on both OCD severity and functional impairment. Relapse at some point during follow-up was not uncommon. Predictors of different outcomes are identified. Conclusions Outcome data from the NordLOTS will be discussed with focus on long-term treatment outcomes, including different groups of outcome trajectories based on OCD type and severity, functional impairment and comorbidity. Various predictors of treatment outcomes will be discussed, including comorbidity and OCD symptom types. Findings will be presented and discussed in relation to their importance for clinical practice, including key issues that remain in the treatment of OCD.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".