Reliability of <scp>DNMSQuest</scp> as a Screening Tool for Mood Disorders in Cervical Dystonia
Bibliographic record
Abstract
BACKGROUND: The high prevalence of mood disorders in cervical dystonia, often unaddressed in botulinum toxin clinics, is a major factor in impaired quality of life. There is a clear need for a brief screening method for identifying these disorders; the Dystonia non-motor symptoms questionnaire (DNMSQuest) has been proposed as such. OBJECTIVE: We aimed to assess the practical utility of the DNMSQuest and compare it with validated rating scales for anxiety, depression and quality of life. METHODS: In 88 patients with cervical dystonia, we compared results from the DNMSQuest with mood rating scales [Beck Anxiety Inventory (BAI), Beck Depression Index (BDI-II) and Hospital Anxiety and Depression Scale (HADS)], quality of life measures [European Quality of Life (EQOL) and European Quality of Life Visual Analogue Scale (EQOLVAS)] and with assessments of dystonia severity [Cervical Dystonia Impact Profile-58 (CDIP58) and Toronto Western Rating Scale for Spasmodic Torticollis (TWSTRS)]. RESULTS: Using a cut off score on the DNMSQuest of 5, we noted that DNMSQuest had a sensitivity of 85% for detecting anxiety and depression using the BAI and BDI-II, and 76% and 78% for anxiety and depression respectively using the HADS. The DNMSQuest correlated strongly with BAI (ρ = 0.715), BDI-II (ρ = 0.658), HADS-Anxiety (ρ = 0.616), HADS-Depression (ρ = 0.706), EQOL (ρ = 0.653) and CDIP-58 (ρ = 0.665). CONCLUSION: The DNMSQuest is a brief, sensitive and non-specific instrument for identifying patients that warrant further review for anxiety and depression and can easily be implemented in a neurologist-run botulinum toxin clinic.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".