Functional outcomes of residual varus alignment versus mechanical alignment in total knee arthroplasty for varus osteoarthritis: A preferred reporting items for systematic reviews and meta-analyses-compliant meta-analysis
Bibliographic record
Abstract
Background One in five patients with mechanical alignment (MA) after total knee arthroplasty (TKA) was reportedly dissatisfied. As constitutional varus knees are common, restoring the patients’ natural residual varus (RV) alignment is as an appealing alternative to neutral MA. This meta-analysis aimed to evaluate the effects of RV alignment on the functional outcomes compared with those of MA in TKA for the knees with varus osteoarthritis. Methods The MEDLINE/PubMed, Cochrane Library, and EMBASE databases were comprehensively searched for papers comparing the effects of RV alignment and MA on the functional outcomes from the time of inception of the databases to July 2020. Studies comparing the functional outcomes in the knees subjected to TKA with RV alignment (case group) and MA (control group) were included. The Knee Society knee and functional scores (KSKS and KSFS, respectively), Western Ontario and McMaster University Osteoarthritis Index (WOMAC), Oxford knee score (OKS), and forgotten joint score (FJS) were compared. Results Seven studies were finally included; all studies showed a low risk of selection bias and provided detailed demographic data. The pooled mean difference in the KSKS (0.06, 95% confidence interval [CI]: −0.14 to 0.27; p = 0.55) and KSFS (0.08, 95% CI: −0.08 to 0.35; p = 0.56) between RV alignment and MA did not significantly differ. The pooled mean differences in the WOMAC (−0.25, 95% CI: −0.57 to 0.07; p = 0.12), OKS (0.06, 95% CI: −0.15 to 0.27; p = 0.56), and FJS (0.41, 95% CI: −0.18 to 1.00; p = 0.18) between the groups were not significant. Conclusion The beneficial effects of RV alignment on the functional outcomes are limited compared to those of MA in TKA for varus osteoarthritis to date. Currently, TKA with neutral MA should be considered as the gold standard.
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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 | 0.028 | 0.064 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.027 | 0.061 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".