Knee Range of Motion as a Discriminatory Tool Indicating Potential Meniscal Tears
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it