Pain catastrophizing as a risk factor for chronic pain after total knee arthroplasty: a systematic review
Bibliographic record
Abstract
BACKGROUND: Total knee arthroplasty (TKA) is a common and costly surgical procedure. Despite high success rates, many TKA patients develop chronic pain in the months and years following surgery, constituting a public health burden. Pain catastrophizing is a construct that reflects anxious preoccupation with pain, inability to inhibit pain-related fears, amplification of the significance of pain vis-à-vis health implications, and a sense of helplessness regarding pain. Recent research suggests that it may be an important risk factor for untoward TKA outcomes. To clarify this impact, we systematically reviewed the literature to date on pain catastrophizing as a prospective predictor of chronic pain following TKA. METHODS: We searched MEDLINE, EMBASE, and PsycINFO databases to identify articles related to pain catastrophizing, TKA, risk models, and chronic pain. We reviewed titles and abstracts to identify original research articles that met our specified inclusion criteria. Included articles were then rated for methodological quality. including methodological quality. Due to heterogeneity in follow-up, analyses, and outcomes reported across studies, a quantitative meta-analysis could not be performed. RESULTS: We identified six prospective longitudinal studies with small-to-mid-sized samples that met the inclusion criteria. Despite considerable variability in reported pain outcomes, pain catastrophizing was identified as a significant predictor of chronic pain persisting ≥3 months following TKA in five of the studies assessed. Limitations of studies included lack of large-scale data, absence of standardized pain measurements, inadequate multivariate adjustment, such as failure to control for analgesic use and other relevant covariates, and failure to report non-significant parameter estimates. CONCLUSION: This study provides moderate-level evidence for pain catastrophizing as an independent predictor of chronic pain post-TKA. Directions for future research include larger, well-controlled studies with standard pain outcomes, identification of clinically-relevant catastrophizing cut-offs that predict pain outcomes, investigation of other psychosocial risk factors, and assessment of interventions aimed to reduce pain catastrophizing on chronic pain outcomes following TKA surgery.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".