Serum IL-6 in combination with synovial IL-6/CRP shows excellent diagnostic power to detect hip and knee prosthetic joint infection
Bibliographic record
Abstract
BACKGROUND: The diagnosis of prosthetic joint infection (PJI) is still a challenge in some patients after total joint replacement. Interleukin-6 (IL-6) strongly participates in the arrangement of the host-bacteria response. Therefore, increased levels of IL-6 should accompany every PJI. PURPOSE: The aim of the study was to show diagnostic characteristics of serum IL-6 for the diagnosis of prosthetic joint infection (PJI). We also compared the diagnostic values of serum IL-6 with synovial IL-6 (sIL-6) and synovial C-reactive protein (sCRP). STUDY DESIGN: We performed a prospective study of 240 patients in whom serum IL-6 was determined before total hip (n = 124) or knee (n = 116) reoperations. The PJI diagnosis was based on the MSIS (Musculoskeletal Infection Society) criteria (2011). Receiver operating characteristic plots were constructed for IL-6, sIL-6, and sCRP. RESULTS: PJI was diagnosed in 93 patients, and aseptic revision was diagnosed in 147 patients. The AUC (area under curve) for IL-6 was 0.938 (95% CI; 0.904-0.971). The optimal IL-6 cut-off value for PJI was 12.55 ng/L. Positive and negative likelihood ratios for IL-6 were 8.24 (95% CI; 4.79-14.17) and 0.15 (95% CI; 0.09-0.26), respectively. The optimal sIL-6 and sCRP cut-off values were 20,988 ng/L and 8.80 mg/L, respectively. Positive and negative likelihood ratios for sIL-6 were 40.000 (95% CI; 5.7-280.5) and 0.170 (95% CI; 0.07-0.417), respectively. Negative likelihood ratio for sCRP was 0.083 (95% CI; 0.022-0.314). CONCLUSIONS: The present study identified the cut-off values for serum/synovial IL-6 and synovial CRP for diagnostics of PJI at the site of THA and TKA and separately for each site. The diagnostic odds ratio for serum/synovial IL-6 and synovial CRP is very good. Simultaneous positivity of serum IL-6 either with synovial IL-6 or synovial CRP almost excludes false negative detection of PJI at the site of interest.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".