Comparison between Obese and Non-obese Cases after Total Knee Arthroplasty Regarding Their Short Term Functional Outcome.
Bibliographic record
Abstract
Introduction:Obesity is considered one of the risk factors of developing knee osteoarthritis(OA). It may influence patient reported outcomes regarding functional score after total knee arthroplasty(TKA) also; cases are at more risk of developing complications like infection and deep venous thrombosis(D.V.T). Aim of the study:This study was held to compare between obese and non-obese cases undergoing TKA regarding functional outcome.Subjects and Methods: This is a prospective case series study in which 30 cases were distributed between two equal groups according to their body mass index(BMI). Non obese group are of BMI ≤ 30 Kg/m2 while obese group are of BMI < 30 Kg/m2. Cases were done in Fayoum university hospitals between September 2020 and July 2022. Functional scores were assessed using the Western Ontario and McMasters Universities Osteoarthritis Index(WOMAC) score before and after surgery by 9 months.Results:The mean BMI in the non-obese group was 28.46 ±SD 1.54 while the mean BMI in the obese group was 36.97 ±SD 5.29. The mean postoperative WOMAC score in the non-obese group (14.6 ±SD 9.46) was less than that of the obese group (20.47 ±SD 8), there was no statistical significance (P value=0.077).Correlation between both groups regarding the change in WOMAC score from before to after surgery does not show statistical significance (P value=0.704).Conclusion:Although the mean WOMAC score in the non-obese group is less than that of the obese group, correlation does not show statistical significance. Obesity does not influence functional outcome after surgery in the short term follow up after 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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".