Diagnostic value of ultrasonography in knee osteoarthritis: A systematic review
Bibliographic record
Abstract
Background: Knee osteoarthritis (KOA) is the most expected diagnosis for an arthropathy that causes discomfort and disability in older adults. Radiography is frequently used to assess patients with KOA and there have been few prior research evaluating the diagnostic efficacy of ultrasonography (US). The current study sought to assess the diagnostic efficacy of the US in identifying various characteristics of KOA in the scientific literature. Materials and Methods: This study was conducted following the Preferred Reporting Items for Systematic Reviews and Meta Analyses statement. A systematic search in PubMed, Web of Science, Scopus, and Embase databases was completed in March 2023. This study focused on the diagnostic value of US in KOA, including sensitivity, specificity, positive predictive value, and negative predictive value. The quality assessment was conducted using the Joanna Briggs Institute critical appraisal tools. Results: Out of 552 records of database searches, finally, two studies met this systematic review's eligibility criteria and were included in the study. Both of the included studies were cross sectional studies. US demonstrated remarkable sensitivity with adequate specificity for the detection of radiographic knee OA; however, it was found not to be an appropriate method for the detection of early KOA. Conclusion: This study as the first systematic review aims to evaluate the diagnostic performance of US in detecting KOA. These findings shed light on the importance of investigating the different US features in the evaluation of KOA to reach appropriate sensitivity and specificity in the diagnosis.
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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 | 0.018 | 0.095 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| 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".