METHODS: Twenty-seven adolescents with OCD and 46 controls completed a predictive-inference task, designed to probe how subjects' actions and confidence ratings fluctuate in response to unexpected outcomes. We investigated how subjects update actions in response to prediction errors (indexing mismatches between expectations and outcomes) and used parameters from a Bayesian model to predict how confidence and action evolve over time. Confidence-action association strength was assessed using a regression model. We also investigated the effects of serotonergic medication.
RESULTS: Adolescents with OCD showed significantly increased learning rates, particularly following small prediction errors. Results were driven primarily by unmedicated patients. Confidence ratings appeared equivalent between groups, although model-based analysis revealed that patients' confidence was less affected by prediction errors compared to controls. Patients and controls did not differ in the extent to which they updated actions and confidence in tandem.
CONCLUSIONS: Adolescents with OCD showed enhanced action adjustments, especially in the face of small prediction errors, consistent with previous research establishing 'just-right' compulsions, enhanced error-related negativity, and greater decision uncertainty in paediatric-OCD. These tendencies were ameliorated in patients receiving serotonergic medication, emphasising the importance of early intervention in preventing disorder-related cognitive deficits. Confidence ratings were equivalent between young patients and controls, mirroring findings in adult OCD research.
METHODS: Based on a preregistered protocol (CRD42022377671), we searched PubMed, Medline, Ovid Embase, APA PsycINFO and Web of Science on 15th August 2022, with no language/type of document restrictions. We included studies reporting accuracy measures (e.g. sensitivity, specificity, or Area under the Receiver Operating Characteristics Curve, AUC) for QbTest in discriminating between people with and without DSM/ICD ADHD diagnosis. Risk of bias was assessed with the Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2). A generic inverse variance meta-analysis was conducted on AUC scores. Pooled sensitivity and specificity were calculated using a random-effects bivariate model in R.
RESULTS: We included 15 studies (2,058 participants; 48.6% with ADHD). QbTest Total scores showed acceptable, rather than good, sensitivity (0.78 [95% confidence interval: 0.69; 0.85]) and specificity (0.70 [0.57; 0.81]), while subscales showed low-to-moderate sensitivity (ranging from 0.48 [0.35; 0.61] to 0.65 [0.52; 0.75]) and moderate-to-good specificity (from 0.65 [0.48; 0.78] to 0.83 [0.60; 0.94]). Pooled AUC scores suggested moderate-to-acceptable discriminative ability (Q-Total: 0.72 [0.57; 0.87]; Q-Activity: 0.67 [0.58; 0.77); Q-Inattention: 0.66 [0.59; 0.72]; Q-Impulsivity: 0.59 [0.53; 0.64]).
CONCLUSIONS: When used on their own, QbTest scores available to clinicians are not sufficiently accurate in discriminating between ADHD and non-ADHD clinical cases. Therefore, the QbTest should not be used as stand-alone screening or diagnostic tool, or as a triage system for accepting individuals on the waiting-list for clinical services. However, when used as an adjunct to support a full clinical assessment, QbTest can produce efficiencies in the assessment pathway and reduce the time to diagnosis.