A study of the effectiveness of ultrasound imaging in the early detection of cartilage degeneration in the knee joint of athletes
Bibliographic record
Abstract
Knee injury is one of the common sports injuries, this paper aims to analyze the performance of ultrasound imaging technology in the assessment of early detection of athletes with knee pain, to provide a reliable assessment index for clinical diagnosis and treatment and an index for judging the efficacy. To analyze the causes of cartilage degeneration in athletes’ knee joints, and to evaluate and compare the ultrasound imaging method with the commonly used clinical assessment (Visual Analogue Scale (VAS) score, Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) score) and imaging assessment (Digital Radiography(DR), Magnetic Resonance Imaging (MRI)) for knee osteoarthritis according to the advantageous performance of ultrasound imaging technology in the examination of knee joint diseases. The correlation between ultrasound assessment and VAS score and WOMAC score, as well as DR assessment and MRI assessment were obtained, respectively. To investigate the value of ultrasonography in the evaluation of patients with osteoarthritis of the knee. The ultrasound scores of the knee were positively correlated with the VAS scores and WOMAC scores, with correlation coefficients of 0.891 and 0.902, respectively. The correlation coefficients of ultrasound ratings with DR ratings and MRI ratings were 0.876 and 0.895, respectively (both > 0.75), which were good correlations.
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.
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 | 0.005 | 0.009 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 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".