[Relationship between intramuscular fat content in the quadriceps muscle and clinical severity in patients with knee osteoarthritis].
Bibliographic record
Abstract
OBJECTIVE: To explore relationship between intramuscular fat content of quadriceps femoris and clinical severity of knee osteoarthritis (KOA). METHODS: Totally 30 KOA patients were selected from February 2021 to June 2021, including 6 males and 24 females, aged with an average of (64.20±9.19) years old, and body mass index (BMI) was (24.92±3.35) kg·m-2. Patients were divided into relative severe leg (RSL) and relative moderate leg (RML) according to severity of pain on visual analogue scale(VAS). Musculoskeletal ultrasound was used to collect muscle images of quadriceps muscles on both sides of the patient, and Image J was used to analyze echo intensity (EI) of each muscle. Both VAS and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were used to assess pain and function. Quadriceps muscle EI on both sides of patients was compared. Pearson correlation analysis was conducted to analyze correlation between quadriceps muscle EI value between RSL and RML, and linear regression was used to analyze relationship between each muscle EI and VAS and WOMA scores of patients. RESULTS: <0.05). CONCLUSION: Intramuscular fat content of quadriceps is closely related to severity of clinical symptoms in KOA patients, and the most obvious one is VM. Therefore, the intramuscular fat content of quadriceps may be an objective indicator to evaluate severity of KOA patients. At the same time, reducing intramuscular fat content of the quadriceps muscle of KOA patients may be a new direction for the prevention and treatment of KOA.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".