Diagnostic accuracy of screening tools for depression and anxiety in cervical dystonia
Bibliographic record
Abstract
INTRODUCTION: Despite their high prevalence and impact, depression and anxiety are not routinely screened for, and accuracy of screening procedures is unknown in adult-onset dystonia. We evaluated accuracy parameters of selected self-rated scales for depression and anxiety in patients with idiopathic cervical dystonia (CD). METHODS: Two-hundred-and-ten patients with idiopathic CD were recruited from 10 movement disorders centers from the US, Canada, Australia, and UK. At the end of each botulinum toxin cycle, participants were administered the Adult Standard Mini-International Neuropsychiatric Interview (MINI) as reference standard for depression and anxiety. Participants completed 8 self-administered index instruments (2 for depression, 2 for anxiety, and 4 combining screening for both). Sensitivity, specificity, positive and negative predictive values, covariate-adjusted area under the receiver operating characteristic curve (AUC), and likelihood ratios were calculated for all instruments. RESULTS: On the MINI, 8.6 % (100 % female) fulfilled criteria for current major depressive disorder and 10.5 % (91 % female) fulfilled criteria for any current disorder amongst panic, social anxiety or generalized anxiety disorders. For depression screening, all tools had an AUC higher than 0.80, with the two most accurate being the BDI-II (AUC 0.91, sensitivity 87.5 %) and the HADS-Depression (AUC 0.88, sensitivity 93.7 %). For anxiety screening, the only instrument showing clinical usefulness was the HADS-Anxiety (AUC 0.82, sensitivity 86.3 %). CONCLUSION: Current major depression can be screened in CD with high degree of accuracy using different self-administered scales, whereas existing screening tools for anxiety perform worse. Dystonia-specific instruments are less accurate than scales developed for the general population.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".