Predictors of persistent postsurgical pain following total knee arthroplasty: A protocol for systematic review and meta-analysis
Bibliographic record
Abstract
Background: Total knee arthroplasty (TKA) is a commonly performed procedure, primarily when knee joints have been damaged by progressive arthritis; however, over 20% of surgical patients develop persistent postsurgical pain (PPSP). We plan to conduct a systematic review and meta-analysis of factors associated with the development of PPSP following TKA.Methods: We will include peer-reviewed cohort or case–control studies that explore, in an adjusted model, factors associated with the development of PPSP after TKA. We will identify eligible studies, in any language, by a systematic search of MEDLINE, EMBASE, CINAHL, AMED, Scopus, SPORTDiscus, and PsycINFO, from inception of each database. Pairs of reviewers will, independently and in duplicate, screen titles and abstracts of identified citations, review the full texts of potentially eligible studies, and extract information from eligible studies. When possible, we will pool estimates of association for all independent variables reported by more than one study and report both an adjusted odds ratio and the absolute risk increase and associated 95% confidence intervals (Cis). We will use the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach to summarize the quality of evidence for all meta-analyses as high, moderate, low, or very low.Discussion: Our results will facilitate identification of patients at risk for the development of PPSP following TKA, highlight promising predictors for further study, and help guide the design of interventional studies to improve prognosis of high-risk patients.
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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 | 0.092 | 0.148 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.040 | 0.003 |
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".