Validity of Selective Tissue Tests for Knee Pathologies: An Evidence-to-Practice Review
Bibliographic record
Abstract
The knee is the most commonly injured joint in sport, inherently meaning the knee is also the joint most frequently evaluated by healthcare providers.Clinicians evaluate and treat the knee as efficiently as possible to prevent long-term disability of the patient.Clinicians rely on physical examination tests such as McMurray's Test, Apley's Test, Joint Line Tenderness, Lachman Test, Anterior Drawer, Pivot Shift Test, and the Ottawa Knee Rules for initial diagnosis and initiation of care.These physical examination tests have varying levels of diagnostic accuracy and validity.Clinicians should know how definite they can be about a diagnosis from physical examination alone based on the tests' validity and reliability.The purpose of this evidence to practice review was to evaluate the validity of the individual and combinations of two or more selective tissue tests for the knee.The authors included systematic reviews and meta-analyses that reported on the diagnostic properties of one or more physical tests for one or more knee disorders.The 17 articles used were screened independently by two reviewers.Each article was appraised using the Assessment of the Methodological Quality of Systematic Reviews (AMSTAR) ranking system.Articles with the highest AMSTAR ranking for each injury and evaluated the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio were used to make recommendations for validity.Physical examination tests of the knee included in the review were found to be most accurate when performed in combination with each other, as they had only low to moderate diagnostic properties.Physical examination tests for the meniscus, ACL, PCL, patellofemoral pain, and knee osteoarthritis are not valid to be used as individual diagnostic tests.The only exemption to this finding is the Lachman test; with a sensitivity of 85%, the Lachman test is suitable to rule out an ACL tear as a stand-alone test.
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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 | 0.039 | 0.277 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".