The association between restoration of tibial slope and total knee arthroplasty outcomes
Bibliographic record
Abstract
Background: The proximal tibia has a characteristic posterior slope, varying between individuals and implicated in biomechanical function. This study evaluated whether restoring the native posterior tibial slope during primary total knee arthroplasty (TKA) improves postoperative functional outcomes. Methods: A retrospective review of 9569 TKA patients from 1998 to 2022 was conducted using a single-centre arthroplasty database. Patients were categorized based on changes in tibial slope: decreased (Group 1, Δ slope <3°), re-created (Group 2, Δ slope -3 to +3°), or increased (Group 3, Δ slope >3°). Preoperative and postoperative anterior and posterior femoral offset ratios were also calculated and analyzed as covariates. Postoperative functional outcomes and range of motion (ROM) were assessed using analyses of covariance (ANCOVAS) and post-hoc pairwise comparisons. Results: Among 609 included patients, tibial slope was decreased in 278 patients (46%, Group 1), re-created in 253 (42%, Group 2), and increased in 78 (13%, Group 3). Group 2 exhibited a 4.8-point higher Knee Society Score at six weeks postoperatively compared to Group 1 (p = 0.004), though scores at other time points and Oxford Knee Scores across all time points showed no significant differences between groups (p > 0.05). Maximal flexion did not differ significantly across groups. These findings were consistent after adjusting for femoral offset ratio changes. Conclusion: Recreation of native tibial slope was associated with a statistically significant but clinically minor improvement in postoperative functional outcome scores. Increasing tibial slope was not associated with improvements in maximal flexion, although this may be confounded by variations in implants utilized. Loe: III (Retrospective cohort study).
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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 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".