Relationship of Personality and Temperament Traits with Pain and Function in Patients with Knee Osteoarthritis
Bibliographic record
Abstract
Objective: There are a few studies on the relationship between personality and temperament types and functionality and pain felt in patients with knee osteoarthritis (OA).This study aimed to determine the relationship between personality and temperament characteristics and pain and function in patients with knee OA. Methods:The study included 126 patients diagnosed with knee OA who met the inclusion criteria.Eysenck Personality Questionnaire Revised-Short Form (EPQR-S) and Type D Personality Scale (DS-14) were used for personality assessment, Temperament Evaluation of Memphis, Pisa, and San Diego Auto-questionnaire (TEMPS-A) was employed for temperament assessment, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) was used for OA pain and general function assessment.Results: Of the participants, 58 (46%) showed Type D personality traits, while depressive temperament was dominant in 18 (14.3%)participants, irritable temperament in 12 (9.5%),and anxious temperament in 16 (17.3%)participants.Those with Type D personality had worse functions, and Type D personality was positively associated with pain and total WOMAC score.Total WOMAC score showed a positive correlation with neuroticism and psychoticism personality traits and cyclothymic and nervous temperament traits.Conclusion: This study demonstrates that pain and total WOMAC score are associated with personality and temperament characteristics in patients with knee OA.In addition to pharmacological and physical therapy, interventions in these areas may be beneficial.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".