Comparative Diagnostic Value of Serological and Synovial Tests for Periprosthetic Joint Infections
Bibliographic record
Abstract
Background: Prompt diagnosis of periprosthetic joint infections (PJIs) is crucial for providing optimal care. Currently, there are no gold-standard tests available. An ideal test would be simple to implement, cost-effective, and readily available. We aimed to determine the best single or combined serological or synovial markers for diagnosing PJIs. Methods: There were 177 of 313 patients who had PJIs between April 2012 and March 2023 and a control group of 60 patients who were included in this retrospective review. The PJIs were diagnosed using Musculoskeletal Infection Society (MSIS) and European Bone and Joint Infection Society (EBJIS) criteria. Serum (C-reactive protein [CRP], white blood-cell [WBC] count, neutrophil-lymphocyte ratio [NLR], polymorphonuclear neutrophil percentage [PMN%]), and synovial fluid (WBC, NLR, PMN%) parameters were compared between the 2 groups. We determined the sensitivity, specificity, area under the curve (AUC), and cutoff values (COV) for each marker. We determined the best combination of markers to diagnose PJIs. There was no statistical significance between the demographic data of the control and treatment groups. Results: The S-CRP had the highest AUC of 0.912 with a COV of 16.15 mg/dL (Sensitivity 79.6%, Specificity 97.8%). The combination of tests, S-CRP, synovial fluid (SF-WBC), and S-NLR demonstrated the highest AUC of 0.946 (Sensitivity 93%, Specificity 90.9%). The COV for SF-WBC was 5.75 cells/μL (AUC 0.803; Sensitivity 70.3%, Specificity 97.1%); S-NLR COV was 3.659 (AUC 0.803; Sensitivity 67.3%, Specificity 88%). Conclusion: We found the combination of S-CRP, SF-WBC, and S-NLR to be valuable in diagnosing PJI with high sensitivities and specificities. It can be easily implemented by clinicians without additional cost or equipment. It is important to use this with a thorough clinical and physical examination as well as other modalities (i.e., MSIS/EBJIS criteria). Level of Evidence: Retrospective Comparative Study-Level III evidence. See Instructions for Authors for a complete description of levels of evidence.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".