Evaluation of the degree of knee joint osteoarthritis in patients with early gray hair
Bibliographic record
Abstract
BACKGROUND: Osteoarthritis (OA) is the most common form of arthritis and one of the causes of pain and disability. The hair graying characteristic correlates strictly with chronological aging and take places to varying degrees in all individuals, disregarding gender or race. AIMS: Comparison of the degrees of clinical and radiologic severity of the knee OA in individuals with early hair graying compared to ordinary individuals. MATERIALS AND METHODS: A total of 60 patients with knee OA and similar demographic characteristics were enrolled in this study. All patients were classified in to 3 age subgroups in each of the case and control groups (30-40 year, 41-50 year, 51-60 year). In the case group, the patients must had early hair graying, too. Knee OA were classified using the Kellgren-Lawrence (KL) grading scale. Western Ontario McMaster University Osteoarthritis index (WOMAC) was applied to assess clinical severity of the knee OA. RESULTS: The mean ± SD of WOMAC index in the case group was 60.7 ± 15.9 and in the control group was 55.3 ± 15.3 (P = 0.1). The mean rank of KL scale in case group was 35.3 and in the control group was 25.6 (P = 0.02). CONCLUSION: Even at the same age of OA onset, the rate of progression of radiological findings and the grade of joint destruction in individuals with early hair graying are greater than normal individuals. However, clinical and functional relevant remain unclear.
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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 | 0.000 |
| 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) | 0.002 | 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".