Psychometric Characteristics of the PainDETECT Questionnaire in Patients Undergoing Total Knee Arthroplasty for Osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Total knee arthroplasty (TKA) is an effective treatment for patients who have advanced osteoarthritis. Accurate preoperative assessment of pain characteristics is crucial for optimizing surgical planning and predicting outcomes. The PainDETECT Questionnaire (PDQ), a pain assessment tool, has demonstrated high reliability and validity, but its application in assessing pain characteristics in TKA patients in mainland China has not been thoroughly investigated. The purpose of this study was to evaluate the psychometric properties of PDQ in pain detection for TKA, providing an effective preoperative pain assessment tool and method for clinical practice. METHODS: This study enrolled 170 participants scheduled for unilateral TKA and administered four self-report questionnaires: the PDQ, the Western Ontario and McMaster Universities Osteoarthritis Index, the five-level EuroQoL Group's five-dimension questionnaire, and the Chinese version of the Central Sensitization Inventory. These assessments were conducted at baseline and one year postoperatively. Standard statistical methods and metrics were used to conduct a series of psychometric evaluations of PDQ, including assessments of reliability, validity, and responsiveness. RESULTS: At the baseline, no significant ceiling or floor effects were observed. Additionally, the internal consistency reliability of PDQ (Cronbach's alpha) was above 0.9, indicating sufficient interitem correlation. The study found a moderate to very strong correlation between PDQ and the Western Ontario and McMaster Universities Osteoarthritis Index and a strong correlation with the five-level EuroQoL Group's five-dimension questionnaire and the Chinese version of the Central Sensitization Inventory, supporting the structural validity of PDQ. Furthermore, PDQ was found to be responsive to changes in pain over time, with a response index ranging from 1.37 to 2.05. CONCLUSIONS: The PDQ assessment tool demonstrated good reliability, validity, and responsiveness in patients who have knee osteoarthritis undergoing TKA and can serve as a useful measurement tool for evaluating pain management strategies and the effectiveness of surgical interventions in clinical research.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".