INFLUENCE OF PREOPERATIVE LIPIDS AND GLUCOSE ON POSTOPERATIVE RECOVERY OF PATIENTS WITH PRIMARY KNEE OSTEOARTHRITIS AFTER TOTAL KNEE ARTHROPLASTY
Bibliographic record
Abstract
Objective To investigate the influence of preoperative lipids and glucose on the postoperative recovery of patients undergoing total knee arthroplasty (TKA) for knee osteoarthritis (KOA). Methods Clinical data were collected from 492 patients with KOA who were hospitalized and underwent surgical treatment in Department of Joint Surgery, The Affiliated Hospital of Qingdao University, from January 2021 to January 2022, including general clinical data and related blood biochemical para-meters in the fasting state before surgery. American Knee Society (AKS) and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores were recorded for the patients at six months after surgery, and WOMAC score <35 was considered good postoperative recovery, while WOMAC score >35 was considered poor postoperative recovery; AKS score >70 was consi-dered good postoperative recovery, and AKS score <70 was considered poor postoperative recovery. A logistic regression analysis was used to investigate the risk factors for low AKS score and high WOMAC score after TKA. Results The multivariate logistic regression analysis showed that in the AKS score group, the high level of serum apolipoprotein B (Apo B) and postoperative heavy physical labor were risk factors for poor postoperative recovery (P<0.05), while in the WOMAC score group, the low level of se-rum apolipoprotein A1 (Apo A1), the high level of triglycerides (TG), female sex, old age, and heavy physical labor were risk factors for poor postoperative recovery (P<0.05). Conclusion High preoperative serum levels of Apo B and TG, the low level of Apo A1, heavy physical labor, and female sex, and old age may increase the risk of poor postoperative recovery. Blood lipid management for KOA patients with hyperlipidemia before TKA may have a favorable impact on the postoperative recovery of pain and function.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".