Cutoff Points for Commonly Used Instruments to Assess Mental Health Problems Among Adults With Tourette’s Syndrome
Bibliographic record
Abstract
OBJECTIVE: Attention-deficit hyperactivity disorder, obsessive-compulsive disorder, depression, and anxiety are highly comorbid in Tourette's syndrome. Cutoff points of screening instruments for these conditions have been validated in the general population. The authors assessed whether established cutoff points on the General Anxiety Disorder-7 (GAD-7) scale; Patient Health Questionnaire-9 (PHQ-9); PHQ-2; Adult ADHD Self-Report Scale, version 1.1 (ASRS v1.1); and Obsessive-Compulsive Inventory (OCI) need to be adjusted for adults with Tourette's syndrome. METHODS: Thirty-six adults with Tourette's syndrome completed these instruments and a diagnostic psychiatric interview. Measures of diagnostic accuracy were calculated (area under the receiver operating characteristic curve [AUC], sensitivity, specificity, positive predictive value, negative predictive value, positive likelihood ratio, and negative likelihood ratio) for each instrument at various cutoffs. Cutoffs for the sample of adults with Tourette's syndrome were suggested by the lowest value derived from a Euclidean distance method. RESULTS: In this sample of adults with Tourette's syndrome, the optimal cutoff points were a GAD-7 score ≥13 (sensitivity, 67%; specificity, 91%; and AUC, 79%), a PHQ-9 score ≥15 (sensitivity, 67%; specificity, 73%; and AUC, 70%), a PHQ-2 score ≥3 (sensitivity, 67%; specificity, 67%; and AUC, 67%), an ASRS v1.1 score ≥14 (sensitivity, 83%; specificity, 77%; and AUC, 80%), and an OCI score ≥63 (sensitivity, 70%; specificity, 89%; and AUC, 79%). The best-performing instrument was the ASRS v1.1, followed by the GAD-7 and OCI; the PHQ-9 and PHQ-2 performed least well in this population. CONCLUSIONS: Further research is needed to adapt screening instruments for the assessment of comorbid conditions among patients with Tourette's syndrome.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".