Diagnostic Validity of Combining History Elements and Physical Examination Tests for Traumatic and Degenerative Symptomatic Meniscal Tears
Bibliographic record
Abstract
BACKGROUND: The current approach to the clinical diagnosis of traumatic and degenerative symptomatic meniscal tears (SMTs) proposes combining history elements and physical examination tests without systematic prescription of imaging investigations, yet the evidence to support this diagnostic approach is scarce. OBJECTIVE: To assess the validity of diagnostic clusters combining history elements and physical examination tests to diagnose or exclude traumatic and degenerative SMT compared with other knee disorders. DESIGN: Prospective diagnostic accuracy study. SETTINGS: Patients were recruited from 2 orthopedic clinics, 2 family medicine clinics, and from a university community. PATIENTS: A total of 279 consecutive patients who underwent consultation for a new knee complaint. METHODS: Each patient was assessed independently by 2 evaluators. History elements and standardized physical examination tests performed by a physiotherapist were compared with the reference standard: an expert physicians' composite diagnosis including a clinical examination and confirmatory magnetic resonance imaging. Participating expert physicians were orthopedic surgeons (n = 3) or sport medicine physicians (n = 2). Penalized logistic regression (least absolute shrinkage and selection operator) was used to identify history elements and physical examination tests associated with the diagnosis of SMT and recursive partitioning was used to develop diagnostic clusters. MAIN OUTCOME MEASURES: Diagnostic accuracy measures were calculated including sensitivity, specificity, positive and negative predictive values, and positive and negative likelihood ratios (LR+/-) with associated 95% confidence intervals (CIs). RESULTS: Eighty patients had a diagnosis of SMT (28.7%), including 35 traumatic tears and 45 degenerative tears. The combination a history of trauma during a pivot, medial knee pain location, and a positive medial joint line tenderness test was able to diagnose (LR+ = 8.9; 95% CI 6.1-13.1) or exclude (LR- = 0.10; 95% CI 0.03-0.28) a traumatic SMT. Combining a history of progressive onset of pain, medial knee pain location, pain while pivoting, absence of valgus or varus knee misalignment, or full passive knee flexion was able to moderately diagnose (LR+ = 6.4; 95% CI 4.0-10.4) or exclude (LR- = 0.10; 95% CI 0.03-0.31) a degenerative SMT. Internal validation estimates were slightly lower for all clusters but demonstrated positive LR superior to 5 and negative LR inferior to 0.2 indicating moderate shift in posttest probability. CONCLUSION: Diagnostic clusters combining history elements and physical examination tests can support the differential diagnosis of SMT. These results represent the initial derivation of the clusters and external validation is mandatory. LEVEL OF EVIDENCE: I.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".