Clinical validation of the parent‐report Toronto Obsessive–Compulsive Scale (TOCS): A pediatric open‐source rating scale
Bibliographic record
Abstract
Background: There is a need to develop a multipurpose obsessive-compulsive disorder (OCD) measure that is useful for cross disorder research and as a reliable clinical rating scale. The current study examined the psychometric properties and established clinical cutoffs for the parent-report version of the Toronto Obsessive-Compulsive Scale (TOCS), a 21-item rating scale of obsessive-compulsive traits. Method: = 391, 51% female). Confirmatory factor analyses, internal consistency reliability, and convergent and divergent validity of the TOCS were examined in the OCD group. Using various scoring approaches, receiver operating characteristic (ROC) analyses were used to establish a clinical cut-off by splitting the OCD group into a discovery sample (166 OCD cases, 164 controls) and a validation sample (184 OCD cases, 227 controls). Classification accuracy and TOCS scores were compared across OCD, ADHD, and ASD groups. Results: The psychometric properties of the TOCS were confirmed. ROC analyses across TOCS scoring approaches in the discovery sample indicated excellent diagnostic discrimination (AUC ≥0.95, sensitivity 77%-92%, specificity 92%-98%). Established cutoffs, when applied in the independent validation sample of OCD cases and controls, showed an overall classification accuracy of 85%-90%. The TOCS total score and symptom count showed good discrimination of OCD from ADHD (AUC ≥0.86) and ASD (AUC ≥0.81). The OCD group scored significantly higher on all TOCS dimensions (except Hoarding) than the ADHD and ASD groups. Conclusion: The TOCS is a reliable and valid rating scale with strong sensitivity and specificity in discriminating OCD cases from controls, as well as from ASD and ADHD. It is a quantitative OCD measure with important clinical and research applications, with particular relevance for cross disorder phenotyping and population-based studies.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".