Assessment of Chronic Postsurgical Pain After Knee Replacement: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: Approximately 20% of patients experience chronic pain after total knee replacement (TKR), yet there is no consensus about how best to assess such pain. This systematic review aimed to identify measures used to characterize chronic pain after TKR.Methods. MEDLINE, Embase, PsycINFO, Cochrane Library, and CINAHL databases were searched for research articles published in all languages from January 2002 to November 2011. Articles were eligible for inclusion if they assessed knee pain at a minimum of 3 months after TKR, yielding a total of 1,164 articles. The data extracted included the study design,country, timings of assessments, and outcome measures containing pain items. The outcome measures were compared with domains recommended by the Initiative on Methods, Measurement, and Pain Assessment in Clinical Trials(IMMPACT) for inclusion in the assessment of chronic pain–related outcomes within clinical trials. Temporal trends were also explored. RESULTS: The review found use of a wide variety of composite and single-item measures, with the American Knee Society Score the most common. Many measures used in published studies did not capture the multidimensional nature of pain recommended by the IMMPACT; of those commonly used, the Western Ontario and McMaster Universities Osteoarthritis Index and Oxford Knee Score were the most comprehensive. Geographic trends were evident, with nation-specific preferences for particular measures. A recent reduction in the use of some clinically administered tools was accompanied by an increased use of patient-reported outcome measures. CONCLUSION: There was wide variation in the methods of pain assessment alongside nation-specific preferences and changing temporal trends in pain assessment after TKR. Standardization and improvements in assessment are needed to enhance the quality of research and facilitate the establishment of a core outcome set.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; both teacher heads agree on what is shown here.
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".