Psychological determinants of problematic outcomes following Total Knee Arthroplasty
Bibliographic record
Abstract
The primary objective of the present study was to examine the role of pain-related psychological factors in predicting pain and disability following Total Knee Arthroplasty (TKA). The study sample consisted of 75 (46 women, 29 men) individuals with osteoarthritis of the knee who were scheduled for TKA. Measures of pain severity, pain catastrophizing, depression, and pain-related fears of movement were completed prior to surgery. Participants completed measures of pain severity and self-reported disability 6 weeks following surgery. Consistent with previous research, cross-sectional analyses revealed significant correlations among measures of pre-surgical pain severity, pain catastrophizing, depression and pain-related fears of movement. Prospective analyses revealed that pre-surgical pain severity and pain catastrophizing were unique predictors of post-surgical pain severity (6-week follow-up). Pain-related fears of movement were predictors of post-surgical functional difficulties in univariate analyses, but not when controlling for pre-surgical co-morbidities (e.g. back pain). The results of this study add to a growing literature highlighting the prognostic value of psychological variables in the prediction of post-surgical health outcomes. The results support the view that the psychological determinants of post-surgical pain severity differ from the psychological determinants of post-surgical disability. The results suggest that interventions designed to specifically target pain-related psychological risk factors might improve post-surgical outcomes.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".