Relationship between pain, quality of sleep, sleep bruxism and patients’ personality among individuals with reported orofacial pain
Bibliographic record
Abstract
The study aimed to determine the relationship between personality traits, orofacial pain (OFP), and headache. Eligible patients underwent one-night videopolysomnography and completed a series of validated questionnaires assessing sleep quality (ISS- Insomnia Severity Scale, PSQI- Pittsburgh Sleep Quality Index, ESS- Epworth Sleepiness Scale), pain intensity (McGill Pain questionnaire, GCPS- Graded Chronic Pain Scale, HIT-6- Headache Impact Test-6, MIDAS- Migraine Disability Assessment, TMD pain screener- Temporomandibular Disorder pain screener), psychoemotional state (KPS- Sense of Stress Questionnaire, PHQ-9 - Patient Health Questionnaire-9, BDI- Beck Depression Inventory, BAI- Beck Anxiety Index, PSS-10- Perceived Stress Scale-10, GAD-7- Generalized Anxiety Disorder-7), personality traits (IPIP-BFM-20 -International Personality Item Pool- Big Five Markers- 20). Patients who exhibited nervousness and hypersensitivity reported higher perceived pain and greater negative impacts on work and daily activities (p = 0,000). Furthermore, a statistically significant relationship was found between subjective sleep quality and emotional stability scales (p = 0,001 for PSQI, p = 0,000 for ISI, p = 0,001 for ESS). However, no such relationship was observed for objective sleep quality, as measured by polysomnography (p > 0,05). The correlation between the emotional stability scale and each of the questionnaires used to examine anxiety, depression, and stress was very strong and corresponded to p = 0.000. Clinical Trial: Relationship Between Selected Parameters and Bruxism www.ClinicalTrials.gov, identifier NCT04214561.
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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 | 0.001 | 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) | 0.001 | 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".