The Use of Biomarkers to Quantify Clinical Response to Total Knee Arthroplasty Interventions: A Systematic Review
Bibliographic record
Abstract
The primary objective of this review was to determine whether the attenuation of the postoperative inflammatory response (PIR) after total knee arthroplasty (TKA) leads to a notable improvement in clinical outcome scores. The secondary objective of this review was to determine the optimal approach in using inflammatory biomarkers, clinical inflammatory assessments, and imaging to quantify the PIR. A systematic literature search of eight major databases was conducted using a predetermined search strategy. C-reactive protein (CRP), interleukin-6 (IL-6), erythrocyte sedimentation rate (ESR), knee surface temperature (KST), and clinical outcome data were collected and graphically displayed. Eighty-six percent of the studies that reported a statistically significant decrease in inflammatory biomarkers in their treatment group demonstrated a concordant notable improvement in clinical outcome scores. Mean CRP, IL-6, ESR, and KST values peaked on postoperative day (POD) 2, POD1, POD7, and POD 1-3, respectively. The PIR is correlated with early pain and function recovery outcomes. Future studies comparing TKA surgical methodologies and perioperative protocols should assess PIR by incorporating inflammatory biomarkers, such as CRP and IL-6, and clinical inflammatory assessment adjuncts, to provide a more comprehensive comparison.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".