Anxiety and depression in spasmodic dysphonia patients
Bibliographic record
Abstract
Objective/Hypothesis Experts used to believe that spasmodic dysphonia (SD) was a psychogenic disorder. Although SD is now established as a neurological disorder, the rates of co‐morbid anxiety and depression range from 7.1% to 62%. Our objective was to study the prevalence and risk factors associated with these mood disorders in SD patients. Study design Retrospective. Methods SD patients who presented for botulinum toxin injections were recruited. Demographic data, Hospital Anxiety and Depression Scale (HADS), Voice Handicap Index‐10 (VHI‐10), General Self‐Efficacy scale (GSES), Disease Specific Self‐Efficacy in Spasmodic Dysphonia scale (DSSE), and Consensus Auditory Perceptual Evaluation of Voice (CAPE‐V) were collected. Results One hundred and forty two patients (age (59.2 ± 13.6) years, 25.4% male) had VHI‐10 of 26.3 ± 6.9 (mean ± standard deviation), GSES 33.2 ± 5.8, CAPE‐V 43.9 ± 20.9, HADS anxiety 6.7 ± 3.7, and HADS depression 3.6 ± 2.8. About 19 (13.4%) and 4 (2.8%) had symptoms of anxiety and depression respectively. Final linear regression model for HADS anxiety (R2 = 32.90%) showed that patients who were less likely to have anxiety symptoms were older age (p < 0.001), male (p = 0.002), have higher GSES (p < 0.001) and lower VHI‐10 (p = 0.004). Final linear regression model for HADS depression score (R2 = 34.42%) showed that patients who were less likely to have depressive symptoms had high DSSES (p < 0.001). Conclusions Prevalence of anxiety (13.4%) and depression (2.8%) in SD were lower than previously reported in the literature. Risk factors for anxiety were: younger age, female gender, lower general self‐efficacy, and higher perceived vocal handicap. The main risk factor for depression was lower disease specific self‐efficacy.
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) | 0.002 | 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 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".