Use of Erythrocyte Sedimentation Rate and C-Reactive Protein Level to Diagnose Infection Before Revision Total Knee Arthroplasty
Bibliographic record
Abstract
BACKGROUND: Despite the widespread use of several diagnostic tests, there is still no perfect test for the diagnosis of infection at the site of a total knee arthroplasty. The purpose of this study was to evaluate the diagnostic test characteristics of the erythrocyte sedimentation rate and C-reactive protein level for the assessment of infection in patients presenting for revision total knee arthroplasty. METHODS: One hundred and fifty-one knees in 145 patients presenting for revision total knee arthroplasty were evaluated prospectively for the presence of infection with measurement of the erythrocyte sedimentation rate and the C-reactive protein level. The characteristics of these tests were assessed with use of two different techniques: first, receiver-operating-characteristic curve analysis was performed to determine the optimal positivity criterion for the diagnostic test, and, second, previously accepted criteria for establishing positivity of the tests were used. RESULTS: A diagnosis of infection was established for forty-five of the 151 knees that underwent revision total knee arthroplasty. The receiver-operating-characteristic curves indicated that the optimal positivity criterion was 22.5 mm/hr for the erythrocyte sedimentation rate and 13.5 mg/L for the C-reactive protein level. Both the erythrocyte sedimentation rate (sensitivity, 0.93; specificity, 0.83; positive likelihood ratio, 5.81; accuracy, 0.86) and the C-reactive protein level (sensitivity, 0.91; specificity, 0.86; positive likelihood ratio, 6.89; accuracy, 0.88) have excellent diagnostic test performance. CONCLUSIONS: The erythrocyte sedimentation rate and the C-reactive protein level provide excellent diagnostic test information for establishing the presence or absence of infection prior to surgical intervention in patients with pain at the site of a knee arthroplasty.
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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 | 0.003 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".