Relationship of hematological and biochemical parameters with WOMAC index to severity of osteoarthritis: A retrospective study
Bibliographic record
Abstract
Aim: Our aim was to investigate whether any hematologic changes that could be detected easily in whole blood counts together with the Western Ontario and McMaster Universities Osteoarthritis score (WOMAC) had diagnostic value for predicting knee osteoarthritis severity.Methods: A retrospective study including a total of 208 knee osteoarthritis patients (112 patients early and 106 patients late osteoarthritis) was carried out. Cut-off values for age, C-reactive protein, neutrophil leukocyte ratio and WOMAC index for osteoarthritis were calculated. A multivariate logistic regression model was used to identify the independent factors of late osteoarthritis. Results: Compared with late osteoarthritis with early osteoarthritis, late osteoarthritis had significantly higher C-reactive protein, neutrophil leukocyte ratio and WOMAC index (p=0.019, p=0.028 and p=0.001, respectively). Area Under Curve was found to be 0.922, 0.533, 0.558 and 0.824 for age, C-reactive protein, neutrophil leukocyte ratio and WOMAC index, respectively. Multilogistic regression analysis was performed with C-reactive protein, neutrophil leukocyte ratio and WOMAC index to determine independent risk factors associated with late osteoarthritis. Odds ratios for neutrophil lymphocyte ratio, C-reactive protein and WOMAC index were found to be 1.317 (95% CI = 1.030-1.682, p = 0.034), 1.055 (95% CI = 1.004-1.108, p = 0.028) and 1.078 (95% CI = 1.056-1.100, p=0.001), respectively. Age, neutrophil leukocyte ratio, C-reactive protein and WOMAC index were statistically significant in predicting late osteoarthritis. Conclusions: Our study suggests that increased neutrophil leukocyte ratio, C-reactive protein and WOMAC index are associated with independent risk factors for late 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 | 0.001 | 0.002 |
| 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 | 0.001 | 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".