Diagnostic Accuracy of Mental Health Screening Tools After Mild Traumatic Brain Injury
Bibliographic record
Abstract
Importance: Mental health disorders are common after mild traumatic brain injury (mTBI) and likely exacerbate postconcussive symptoms and disability. Early detection could improve clinical outcomes, but the accuracy of mental health screening tools in this population has not been well established. Objective: To determine the diagnostic accuracy of the Patient Health Questionnaire-9 (PHQ-9), Generalizaed Anxiety Disorder-7 (GAD-7), and Primary Care PTSD (Posttramatic Stress Disorder) Screen for Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) (PC-PTSD-5) in adults with mTBI. Design, Setting, and Participants: This diagnostic study was performed as a secondary analysis of a cluster randomized clinical trial. Self-report mental health screening tools (PHQ-9, GAD-7, and PC-PTSD-5) were administered online 12 weeks after mTBI and compared against a structured psychodiagnostic interview (Mini-International Neuropsychiatric Interview for DSM-5 (MINI) over videoconference at the same time. Adults with mTBI (N = 537) were recruited from February 1, 2021, to October 25, 2022. Main Outcomes and Measures: Presence of a major depressive episode, anxiety disorders, and PTSD were determined by a blinded assessor with the MINI. Diagnostic accuracy statistics were derived for the PHQ-9, GAD-7, and PC-PTSD-5. Findings were disaggregated for participants with and without persistent postconcussion symptoms (PPCS) by International and Statistical Classification of Diseases, Tenth Revision criteria. Results: Data were available for 499 of 537 trial participants, 278 (55.7%) of whom were female; the mean (SD) age was 38.8 (13.9) years. Each screening questionnaire had strong diagnostic accuracy in the overall sample for optimal cut points (area under the curve [AUC], ≥0.80; sensitivity, 0.55-0.94; specificity, 0.64-0.94). The AUC (difference of 0.01-0.13) and specificity (difference, 5-65 percentage points) were lower in those with PPCS present compared with PPCS absent, but the prevalence of at least 1 mental health disorder was 3 to 5 times higher in patients with PPCS present. The GAD-7 had slightly better performance than the PC-PTSD-5 for detecting PTSD (AUC, 0.85 [95% CI, 0.80-0.89] vs 0.80 [95% CI, 0.72-0.87]). The optimal cutoff on the PHQ-9 was 5 or more symptoms experienced on more than half of days; on the GAD-7, a total score of at least 7. Conclusions and Relevance: The findings of this diagnostic study suggest that the PHQ-9, GAD-7 and PC-PTSD-5 accurately screen for mental health disorders in patients with mTBI. Future research should corroborate optimal test cutoffs for this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".