THE ROLE OF AMINO ACIDS IN KNEE OSTEOARTHRITIS
Bibliographic record
Abstract
Background: Osteoarthritis is an important public health problem and the most common musculoskeletal disease in the World. The pathogenesis and etiology of osteoarthritis is still unclear. We aimed to make an Amino acids analysis that will contribute to the pathogenesis, diagnosis and treatment of knee osteoarthritis. Materail and Methods:The study included according to the radiological grading scale of Kellgren-Lawrence, 30 patients at Grade 1-2; Group 1 (Grade 1-2), 30 patients at Grade 3-4; Group 2 (Grade 3-4), 30 healthy controls; Group 3. We compared between groups age, sex, body mass index, Western Ontario and McMaster Universities, Short form-36 findings, and plasma-free amino acid levels. Results:A comparison of the serum norvaline, leucine, isoleucine, allo-isoleucine, cystathionine, phenylalanine, 1-methyl hystidine, arginine, alanine, cystine, valine, threonine, and tryptophane levels of the knee osteoarthritis and control groups compared a statistically significant difference (p=0.000, p=0.000, p=0.000, p=0.000, p=0.044, p=0.003, p=0.000, p=0.035, p=0.010, p=0.011, p=0.000, p=0.000, p=0.003). Conclusions We consider that, norvaline leucine; isoleucine, allo-isoleucine, cystathionine, phenylalanine, 1-methyl hystidine, arginine, alanine, cystine, valine, threonine, and tryptophane amino acids could be, as potential systemic serum biomarkers for diagnosis of knee osteoarthritis.
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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 | 0.000 |
| 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) | 0.001 | 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".