Combination Tests in the Diagnosis of Chronic Periprosthetic Joint Infection
Bibliographic record
Abstract
BACKGROUND: Our objective was to identify combination tests used to diagnose chronic periprosthetic joint infection (PJI) and develop a stepwise decision-making tool to facilitate diagnosis. METHODS: We conducted a systematic review of existing combinations of serum, synovial, and tissue-based tests for diagnosing chronic PJI after hip or knee replacement. This work is an extension of our systematic review of single tests, from which we chose eligible studies that also described the diagnostic performance of combination tests. RESULTS: Thirty-seven eligible articles described the performance of 56 combination tests, of which 8 combinations had at least 2 studies informing both sensitivity and specificity. We also identified 5 types of combination tests: (1) a type-I Boolean combination, which uses Boolean logic (AND, OR) and usually increases specificity at the cost of sensitivity; (2) a type-II Boolean combination, which usually increases sensitivity at the cost of specificity; (3) a triage-conditional rule, in which the value of 1 test serves to triage the use of another test; (4) an arithmetic operation on the values of 2 tests; and (5) a model-based prediction rule based on a fitted model applied to biomarker values. CONCLUSIONS: Clinicians can initiate their diagnostic process with a type-II Boolean combination of serum C-reactive protein (CRP) and interleukin-6 (IL-6). False negatives of the combination can be minimized when the threshold is chosen to reach 90% to 95% sensitivity for each test. Once a joint infection is suspected on the basis of serum testing, joint aspiration should be performed. If joint aspiration yields a wet tap, a leukocyte esterase (LER) strip is highly recommended for point-of-care testing, with a reading of ++ or greater indicating PJI; a reading below ++ should be followed by one of the laboratory-based synovial tests. If joint aspiration yields a dry tap, clinicians should rely on preoperative tissue culture and histological analysis for diagnosis. Combinations based on triage-conditional, arithmetic, and model-based prediction rules require further research. LEVEL OF EVIDENCE: Diagnostic Level III. See Instructions for Authors for a complete description of levels of evidence.
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 | 0.038 | 0.142 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.008 |
| Bibliometrics | 0.014 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".