The role of perceived injustice in the prediction of pain and function after total knee arthroplasty
Bibliographic record
Abstract
Emerging evidence suggests that the appraisal of pain and disability in terms of justice-related themes contributes to adverse pain outcomes. To date, however, research on the relation between perceived injustice and pain outcomes has focused primarily on individuals with musculoskeletal injuries. The primary aim of this study was to investigate the role of perceived injustice in the prediction of pain and disability after total knee arthroplasty (TKA). The study sample consisted of 116 individuals (71 women, 45 men) with osteoarthritis of the knee scheduled for TKA. Participants completed measures of pain severity, physical disability, perceptions of injustice, pain catastrophizing, and fear of movement before surgery, and measures of pain and disability 1 year after surgery. Prospective multivariate analyses revealed that perceived injustice contributed modest but significant unique variance to the prediction of postsurgical pain severity, beyond the variance accounted for by demographic variables, comorbid health conditions, presurgical pain severity, pain catastrophizing, and fear of movement. Pain catastrophizing contributed significant unique variance to the prediction of postsurgical disability. The current findings add to a growing body of evidence supporting the prognostic value of perceived injustice in the prediction of adverse pain outcomes. The results suggest that psychosocial interventions designed to target perceptions of injustice and pain catastrophizing before surgery might contribute to more positive recovery trajectories after TKA.
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 | 0.002 | 0.019 |
| 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 | 0.001 |
| 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".