Preoperative Determinants of Patient-reported Pain and Physical Function Levels Following Total Knee Arthroplasty: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: A sound knowledge of the determinants of total knee arthroplasty (TKA) outcomes could help in patient selection, preparation and education. We aimed to assess the current status of the literature evaluating preoperative determinants of early and medium term patient-reported pain and disability following TKA. METHOD: A search in Medline, Pubmed, Embase and CINAHL until October 2014 was undertaken. Selection criteria included: 1- participants undergoing primary unilateral TKA with a follow-up from 6 months to 2 years, 2- validated disease-specific patient-reported outcome measures assessing pain and/or function used as outcome measure and 3- identification of preoperative determinants obtained via multivariate analyses. Risk of bias was assessed using a modified version of the Methodology checklist for prognostic studies. RESULTS: Thirty-three prognostic explanatory studies were included. Mean total score of the methodological quality was 80.7±12.2 %. Sociodemographic and psychosocial determinants included greater socioeconomic deprivation (both studies), greater levels of depression and/or anxiety (7 out of 10 studies) and greater preoperative pain catastrophizing (all 3 studies). Significant clinical determinants included worse pre-operative knee related pain or disability (20 out of 22 studies), presence or greater levels of comorbidity (12 out of 23 studies), back pain (4 out of 5 studies) and lower general health (all 11 studies). CONCLUSION: Several significant determinants of short to medium-term pain and functional outcomes following TKA have been summarized by studies with moderate-to-high methodological quality. No conclusions can be reached regarding the strength of the associations between significant determinants and TKA results because of heterogeneity of study methodologies and results. Further high-quality research is required.
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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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.009 | 0.012 |
| 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".