The association of folate deficiency with clinical and radiological severity of knee osteoarthritis
Bibliographic record
Abstract
CONTEXT: Folate deficiency is often observed in patients with inflammatory diseases, raising questions about its role in knee osteoarthritis (OA) progression. OBJECTIVES: This study aimed to assess the association of folate deficiency with the clinical and radiological severity of knee OA. METHODS: A prospective cross-sectional study was conducted from January 1, 2019 to January 1, 2020. Primary knee OA patients referred to orthopedic clinics in Zabol, Iran were included. Radiographic severity was gauged utilizing the Kellgren-Lawrence (KL) classification. For clinical severity, patients completed the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) questionnaire. IBM SPSS v.27 facilitated the statistical analysis. RESULTS: Forty-nine knee OA patients, averaging 67.45±13.44 years in age, were analyzed. Spearman correlation analysis revealed a negative correlation between folate levels and both WOMAC and KL scores. The correlation was stronger between folate and KL score (Spearman correlation coefficient: -0.75) than between folate and WOMAC total score (Spearman correlation coefficient: -0.46). Additionally, a significantly higher KL score was observed in patients with folate deficiency (p=0.004). CONCLUSIONS: Our study highlights a significant correlation between folate deficiency and increased severity of OA, which is evident in radiological and clinical assessments. These findings suggest that folate plays a key role in OA pathogenesis and could be a modifiable factor in its management.
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 | 0.001 | 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 | 0.000 | 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".