Cervical dystonia patients with psychiatric classification: Despite dystonia improvement less improvement in other domains after DBS surgery
Bibliographic record
Abstract
Background Patient satisfaction of deep brain stimulation (DBS) for cervical dystonia (CD) is heterogeneous. A high prevalence of psychiatric disorders in patients with CD is well-established. The presence of psychiatric classification in CD may affect the outcomes of DBS treatment. Methods A cohort of 49 patients with CD and GPi-DBS was retrospectively studied in two centers. Psychiatric history was obtained from patient records. Pre- and post-operative Toronto Western Spasmodic Torticollis Rating Scores (TWSTRS, range 0–85) were compared between patients with and those without psychiatric classification. The TWSTRS disability and pain sub-scores were combined to evaluate non-motor improvement. The severity sub-score was used to assess motor improvement. Results Twenty (40.8 %) patients had a psychiatric classification, predominantly major depressive disorder and anxiety disorders. Following DBS treatment, the overall mean (± SD) improvement on the TWSTRS was 38.0 ± 29.2 %. Significantly, patients with a psychiatric classification experienced less improvement in the non-motor domain than the patients without a psychiatric classification (29.1 ± SD 38.2 % [range −41.7 to 96.6 %] vs. 51.9 ± 33.6 % [range −8.6 to 100.0 %]; p = 0.02). Conclusion Our findings indicate that CD patients with psychiatric classifications experience less non-motor improvement following DBS. Psychiatric comorbidities could influence the lacking experience of successful DBS treatment despite good motor outcome. Therefore, it is important to establish these comorbidities in CD patients undergoing DBS with respect to expectation management and treatment if necessary.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".