Joint Line Fullness and Meniscal Pathology
Bibliographic record
Abstract
BACKGROUND: Meniscal tears have been associated with meniscal cysts and fullness of the knee joint line on physical examination. HYPOTHESIS: Joint line fullness is an accurate, sensitive, and specific test to detect meniscal tears. STUDY DESIGN: Prospective cohort study. METHODS: One hundred consecutive patients undergoing knee arthroscopy were included. All had physical examinations documenting the presence of joint line fullness, joint line tenderness, and the McMurray sign. Arthroscopy was the gold standard for tears. Accuracy, sensitivity, and specificity were calculated and correlated with type of tear. Sixty-one patients had a magnetic resonance imaging preoperatively (the gold standard for determining the presence of a cyst). RESULTS: Meniscal tears were found in 67 patients at arthroscopy. The accuracy, sensitivity, and specificity of joint line fullness were, respectively, 73%, 70%, and 82% in detecting meniscal tears; 68%, 87%, and 30% for joint line tenderness; and 47%, 32%, and 78% for the McMurray sign. The highest positive predictive value for detecting a tear was 88% for joint line fullness, compared with 77% for joint line tenderness and 76% for the McMurray sign. However, joint line fullness did not correlate well with the presence of a cyst, with a low positive predictive value (29%). Of those patients with joint line fullness on physical examination, 89% had a horizontal cleavage component of their tear at arthroscopy. CONCLUSION: Joint line fullness is an accurate, sensitive, and specific test to detect meniscal tears. CLINICAL RELEVANCE: The findings support the routine use of joint line fullness during physical examination along with other common tests to improve the accuracy of clinically diagnosing meniscal tears.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".