Unravelling dystonic pain; a mixed methods survey to explore the language of dystonic pain and impact on life
Bibliographic record
Abstract
PURPOSE: Dystonia is a neurological disorder characterised by involuntary muscle contractions. Pain is the primary non-motor symptom, and limited studies have investigated how dystonic pain is experienced. This study aimed to investigate how people with isolated dystonia describe their pain and compare across subgroups of dystonia. METHODS: social media asking participants to describe their pain in their own words, complete the McGill Pain Questionnaire (MPQ), and answer demographic questions. Thematic analysis identified common themes and frequencies were calculated for demographic and MPQ data. RESULTS: One-hundred and sixty-five respondents were included (mean age 51 years, 85% female). Thematic analysis identified four major themes "Physical sensations", "Temporal features", "Destruction", "Impact on life" with several sub-themes. The most chosen MPQ descriptor was "exhausting" followed by "tight," "sharp," "pulling," and "aching". The most common descriptors showed similar prevalence across subgroups of dystonia. CONCLUSION: As no objective tests for pain exist, pain sufferers must use language to describe their pain experience. People with isolated dystonia used sensory words combined with metaphorical language to detail temporal features of pain, as well as destructive internal battles or feelings of external forces acting upon them, and the significant toll pain has on everyday life. Implications for rehabilitationPain is a common and debilitating non-motor symptom for people living with dystonia and should be discussed in a persons treatment plan.Pain sufferers use language to discuss their pain experience with others and report they don't feel well understood by others including health professionals.People with dystonic pain commonly described physical sensations, temporal features, destructive forces, and the impact on life caused by their pain.Findings suggest the experience of pain with dystonia is varied and better pain management options for people with dystonia are needed.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".