High variation among clinical studies in the assessment of physical function after knee replacement: a systematic review
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to summarise the current use of outcome measures for the assessment of physical function after knee joint replacement. METHODS: A systematic approach following the PRISMA guidelines was used. Literature search was performed on MEDLINE database via PubMed and on Epistemonikos. Clinical trials (level of evidence I-II) on knee joint replacement reporting data on the 'physical function' domain published between January 2017 and June 2022 were included. Descriptive statistics were used to summarise the evidence. RESULTS: In the 181 articles that met the inclusion criteria, 49 different outcome measurements were used to evaluate clinical outcomes after knee joint replacement. The most frequently adopted patient-reported outcome measures (PROMs) were the Knee Society Score (KSS) (78 studies; 43.1%), the Western Ontario and McMaster Universities (WOMAC) Arthritis Index (62 studies; 34.3%), the Oxford Knee Score (OKS) (51 studies; 28.2%) and the Knee Injury and Osteoarthritis Outcome Score (KOOS) (36 studies; 20%). The most frequently used performance-based outcome measures (PBOMs) were the Timed-Up-and-Go (TUG) test (30 studies; 16.6%) and the 6-min-walk test (6MWT) (21 studies; 11.6%). Among impairment-based outcome measures (IBOMs), range of motion (ROM) was the most used (74 studies; 40.9%). CONCLUSION: There is considerable variation among clinical studies regarding the assessment of the physical function of patients after knee joint replacement. PROMs were found to be the most commonly adopted outcome measures; however, no single PROM was used in more than half of the papers analysed. LEVEL OF EVIDENCE: Level II, systematic review of level I-II studies.
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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 | 0.085 | 0.263 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".