Fluid Homeostasis May Predict the Prognosis of Non-infectious Fever After Total Knee Arthroplasty Within 7-Day: A Retrospective Cohort Study
Bibliographic record
Abstract
Background: In the perioperative management of Total Knee Arthroplasty (TKA), postoperative fever has always been a concern. Current research focuses on infectious fever, and there is no relevant research on the occurrence of non-infectious fever (NIF) and its risk factors. Hence, the aim of this study was to clarify the risk factors for NIF after TKA, and construct an easy-to-use nomogram. Methods: A retrospective cohort study was conducted. Consecutive patients undergoing primary unilateral TKA were divided into the non-infectious fever group and the control group. Clinicopathological characters were collected from electronic medical records. Univariate Logistic regression was used to analyze the related independent risk factors. The optimal threshold for each selected factor and combined index was determined when the Youden index achieved the highest value. And the predictive nomogram was developed by these independent factors. Results: Ultimately, 146 patients were included in this study. Of them, 57 (39.04%) patients experienced NIF. Results of the univariable logistic regression analysis indicated that intraoperative blood loss (OR, 1.002; 95% CI, 1.000–1.0004), postoperative drainage fluid volume (OR, 1.003; 95% CI, 1.001–1.006) and frequency of blood transfusion (n = 1; OR, 0.227; 95% CI, 0.068–0.757) were independent risk factors of NIF occurrence. The predictive nomogram that incorporated the above independent risk factors was developed, and it yielded an areas under the curves (AUC) of 0.731 (95% CI: 0.651–0.801; P < 0.0001) with 54.39% sensitivity and 82.02% specificity. Conclusions: Non-infectious fever after TKA prolongs the time of antibiotic use and hospital stay. Our results demonstrated that the nomogram may facilitate to predict the individualized risk of NIF occurrence within 7-day by intraoperative blood loss, postoperative drainage fluid volume and frequency of blood transfusion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".