Assessment of functional mobility and body mass index among patients with a total knee replacement: a retrospective study in Indian population
Bibliographic record
Abstract
Background: Obesity is associated with an increased risk of osteoarthritis, and the incidence of obese patients requiring a total knee replacement (TKR) has increased in recent years. A high body mass index (BMI) may influence post‐TKR rehabilitation outcomes. The aim of the present study was to assess the effects of obesity on functional mobility outcomes following post‐TKR rehabilitation in Asian patients where BMI was not as high as those reported in similar studies performed other countries other than Asian. Methods: A total of 100 patients were categorized as normal weight (n=11), overweight (n=10), class I obese (n=28), or class II obese (n=32), class III obese (n=19). Patients were retrospectively followed up for 6 months after undergoing TKR followed by 2 months of active rehabilitation. Outcome measures were recorded at baseline and at the 2‐month and 6‐month follow-up assessments and included the Western Ontario and McMaster Universities Osteoarthritis Index and the following tests: functional reach, single‐leg stance, ten‐meter walk, timed up and go, chair rise, and stair climbing. Results: A 4×3 (group×time) repeated‐measures analysis of variance showed significant improvement in all of the outcome measures for all of the BMI groups at the 2‐month and 6‐month follow-up assessments (p<0.05 for all). No significant intergroup differences at the 2‐month and 6‐month follow-up assessments were observed for any of the mobility measures except the functional reach and single‐leg stance (p<0.05). Conclusions: Patients with class II/III obesity benefit from early post‐TKR outpatient rehabilitation and respond well. Also, the patients with lower BMIs showed significant improvements and patients with a high BMI might require additional balance-based exercises in their post-TKR rehabilitation protocols.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".