Blood and urinary cytokine balance and renal outcomes at orthopaedic surgery
Bibliographic record
Abstract
Background: In patients undergoing orthopaedic trauma surgery, acute kidney injury (AKI) can develop post-operatively and is a major cause of increased mortality and hospital stay time. Development of AKI is associated with three main processes: inflammation, ischaemia-reperfusion injury (IRI) and hypoperfusion. In this study, we investigated whether ratios of urine and blood anti-inflammatory biomarkers and biomarkers of hypoperfusion, IRI and inflammation are elevated in patients who develop post-trauma orthopaedic surgery acute kidney injury (PTOS-AKI). Methods: Blood and urinary biomarkers of inflammation, hypoperfusion and IRI were analysed in 237 patients undergoing orthopaedic fracture surgery pre- and post-operatively. Biomarker ratios were compared between non-PTOS-AKI and PTOS-AKI patients. Results: Multiple inflammatory biomarkers were significantly elevated in PTOS-AKI patients compared to non-PTOS-AKI patients. When urine anti-inflammatory biomarkers were expressed as biomarker ratios with biomarkers of inflammation, hypoperfusion and IRI, multiple ratios were lower in PTOS-AKI patients. In contrast, blood anti-inflammatory biomarkers when expressed as ratios with blood proinflammatory biomarkers were elevated in PTOS-AKI patients. Discussion: Reductions in ratios of urine anti-inflammatory and proinflammatory biomarkers in PTOS-AKI patients suggest that the renal anti-inflammatory response is protective against the proinflammatory response in patients who do not develop PTOS-AKI. Detection of proinflammatory and anti-inflammatory biomarkers both pre- and post-operatively may be useful in detecting patients at risk of developing AKI after orthopaedic surgery.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".