Challenges in the diagnosis and treatment of pediatric obsessive–compulsive disorder
Bibliographic record
Abstract
Obsessive-compulsive disorder (OCD) affects 1%-3% of children worldwide and has a profound impact on quality of life for patients and families. Although our understanding of the underlying etiology remains limited, data from neuroimaging and genetic studies as well as the efficacy of serotonergic medications suggest the disorder is associated with the fundamental alterations in the function of cortico-striato-thalamocortical circuits. Significant delays to diagnosis are common, ultimately leading to more severe functional impairment with long-term developmental consequences. The clinical assessment requires a detailed history of specific OCD symptoms as well as psychiatric and medical comorbidities. Standardized assessment tools may aid in evaluating and tracking symptom severity and both individual and family functioning. In the majority of children, an interdisciplinary approach that combines cognitive behavioral therapy with a serotonin reuptake inhibitor leads to meaningful symptom improvement, although some patients experience a chronic, episodic course. There are limited data to guide the management of treatment-refractory illness in children, although atypical antipsychotics and glutamate-modulating agents may be used cautiously as augmenting agents. This review outlines a clinical approach to the diagnosis and management of OCD, highlighting associated challenges, and limitations to our current knowledge.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".