Serum amyloid A and fibrinogen as markers for early detection of surgical site infection associated with internal fixation in the horse
Bibliographic record
Abstract
The objective of this study was to determine the diagnostic ability of serum amyloid A (SAA) and fibrinogen for early detection of surgical site infection (SSI) after equine internal fixation. Horses undergoing internal fixation for fracture, arthrodesis, or osteotomy with internal fixation for limb deformity were included in the study. SAA and fibrinogen were measured on blood samples preoperatively and on days 1, 3, 5, 7, 10, and 14 postoperatively. Statistical analysis included use of Spearman's rank correlation, logistic regression, and calculating the area under the receiver operating characteristic (ROC) curve. SAA and fibrinogen measurements were both associated with SSI, with SAA being considered an excellent marker (area under the ROC curve 0.8) and fibrinogen being considered acceptable (<0.8). As the amount of time postoperatively increased, SAA elevations indicated a higher likelihood of SSI (area under the ROC curve 0.8 compared with fibrinogen 0.7). SAA and fibrinogen were predictive markers of SSI and SAA is of greater diagnostic utility when compared with fibrinogen. Persistent elevations of SAA postoperatively are associated with the development of SSI. Serial monitoring of SAA can be used to help predict the development of SSI in horses undergoing internal fixation. This may lead to earlier suspicion, and therefore recognition and treatment of SSI.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".