Bibliographic record
Abstract
Introduction: Tic spectrum disorders-Gilles de la Tourette's syndrome (GTS) and Persistent (Chronic) Motor Tic Disorder (CMTD)-are neurodevelopmental conditions characterized by recurrent motor and/or vocal tics persisting for at least 1 year. While tics are the primary symptom, pain related to tics is an important yet often overlooked aspect of these disorders. Pain can result from the repetitive nature of tics, leading to muscle strain, joint stress, or even injury due to the forceful execution of movements. Additionally, pain resulting from rapid and repeated movements or vocalizations can contribute to chronic discomfort, significantly affecting daily functioning and quality of life. Despite its impact, tic-related pain is not always addressed in clinical practice, leaving individuals struggling to find effective management strategies. The aims of this study were to assess tic-related pain, pain coping strategies and beliefs, and to investigate whether individuals experiencing pain seek professional help and how effective they perceive such interventions to be. Methods: A total of 76 participants diagnosed with GTS and CMTD participated in the study. The following scales were used: Yale Global Tic Severity Scale (YGTSS), Visual Analog Scale (VAS), Short-Form McGill Pain Questionnaire-2 (SF-MPQ-2), Pain Coping Strategies Questionnaire (CSQ), Beliefs Questionnaire for Pain Control (BPCQ), and a survey containing demographic and health data and questions about pain management and its effectiveness. Results: 73.7% of participants reported tic-related pain; almost all of them declared pain located in more than one part of the body. Affective pain domain was the highest scored by both men and women. Coping self-statements was the most common coping strategy chosen by men, whereas catastrophizing was preferred by women. Respondents rated internal factors as the most important in pain management and external factors, particularly medical professionals, as the least important. This aligns with their experience, as fewer than one in five found professional interventions effective in relieving pain. Conclusion: Pain should be recognized as a common comorbid aspect of tic disorders and therefore both pharmacological and non-pharmacological interventions should include pain management. Chronic pain can significantly impair functioning in all areas of life and recommendations for the management of tic-related pain need to be developed.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".