Knee Range of Motion as a Discriminatory Tool Indicating Potential Meniscal Tears
Bibliographic record
Abstract
BACKGROUND: Primary care physicians often encounter patients with knee pain and are faced with the dilemma of whether to refer patients to a specialist. Meniscal tears are the most common intraarticular knee injury but are challenging to accurately diagnose because of a lack of quantitative, accurate, and easy-to-administer tests. We conducted a retrospective medical record review to evaluate whether measurement of knee range of motion (ROM) via goniometry could discriminate between healthy and meniscus-altered knees. METHODS: A total of 110 adult patients met the inclusion criteria: age ≥18 years; no history of contralateral knee pain, injury, or surgery; ROM data collected using a goniometer on both knees at the time of diagnosis; and a confirmed diagnosis of meniscus tear via magnetic resonance imaging. The following variables were obtained from medical records: age, sex, body mass index (BMI), ROM for both knees, surgical treatment, insurance coverage, Ahlbäck x-ray grades, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and the Oxford Knee Score (OKS). RESULTS: The majority of patients (96.4%) exhibited a ≥10° difference in flexion between asymptomatic and symptomatic knees. No significant relationships were observed between age, BMI, and the decision to undergo surgery and the difference in flexion or extension ROM. Both the WOMAC and the OKS were significantly correlated with the degree of loss of flexion ROM. CONCLUSION: The results suggest that knee flexion ROM may be a valuable tool for determining which patients presenting with new-onset ipsilateral knee pain should be referred to a specialist. Further investigation to determine the reliability and accuracy of knee ROM as a screening measure is warranted.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".