Psychometric Properties of the Brief Pain Inventory Among Patients With Osteoarthritis Undergoing Total Knee Arthroplasty Surgery
Bibliographic record
Abstract
BACKGROUND: Knee osteoarthritis (OA) is characterized by pain and functional restrictions, necessitating precise and reliable pain evaluation for effective disease surveillance and postoperative treatment appraisal. METHODS: This investigation recruited 110 participants who were slated to receive unilateral total knee arthroplasty (TKA) and administered 3 self-reported questionnaires: the Brief Pain Inventory (BPI), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and 5-level EuroQoL Group's 5-dimension questionnaire (EQ-5D-5L), at baseline and 1 year after surgery. Using standard statistical methods and indicators, the BPI was subjected to a battery of psychometric evaluations, including assessments of reliability, validity, and responsiveness. RESULTS: At baseline, there were no significant ceiling or floor effects observed. Additionally, the internal consistency reliability (Cronbach's alpha) of the BPI was above 0.8, suggesting that the questionnaire items are adequately related to one another. The study found moderate to very strong correlations between the pain and physical function domains of the BPI and Western Ontario and McMaster Universities Osteoarthritis Index, as well as a strong correlation between the functional interference dimension of the BPI and the EQ-5D, supporting the construct validity of the BPI. Also, the BPI was found to be responsive to changes in pain over time, with a responsiveness index ranging from 2.55 to 3.19. CONCLUSION: The BPI assessment tool demonstrated good reliability, validity, and responsiveness in knee osteoarthritis patients who have undergone TKA and can be a useful measurement tool in clinical research to evaluate the effectiveness of pain management strategies and surgical interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".