Correlation between the severity of osteoarthritis and the serum levels of IL-4 and sIL-4R
Bibliographic record
Abstract
Objective To explore the correlation between the severity of osteoarthritis and the serum levels of interleukin-4 (IL-4) and soluble IL-4 receptor (sIL-4R). Methods Fifty patients with osteoarthritis hospitalized from May 2011 to May 2013 were selected as study group, and 50 healthy individuals during the corresponding period were selected as control group. The serum levels of IL-4 and sIL-4R were determined by EILISA and compared between the two groups. The severity of osteoarthritis of patients in the study group was assessed by Kellgren and Lawrence (K&L) grading system and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC). The relationship between both K&L grade and WOMAC scores and the serum levels of IL-4 and sIL-4R was analyzed. Results The serum level of sIL-4R in the study group (82.12±11.24ng/ml) was significantly higher than that in control group (30.67±5.23ng/ml, P<0.05), but the levels of IL-4 in two groups were similar (14.39±7.14 and 15.23±6.28ng/L, P>0.05). The serum level of sIL-4R in the study group was positively correlated with the WOMAC scores (r=0.313, P<0.05). The sIL-4R levels were increased gradually along with the increase in K&L grade, and the difference between different grades was significant (P<0.05). Conclusion The serum level of sIL-4R is correlated with the severity of osteoarthritis, and may be used as an evaluation indicator of severity of osteoarthritis.\n\t\t\n\t\tDOI: 10.11855/j.issn.0577-7402.2015.01.14
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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 | 0.002 |
| 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".