Does Postoperative Mechanical Axis Alignment Have an Effect on Clinical Outcome of Primary Total Knee Arthroplasty? A Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: There is an ongoing debate whether patients with constitutional varus should be restored to neutral mechanical alignment following total knee arthroplasty (TKA). OBJECTIVE: The aim of this retrospective cohort study is to determine whether mild unintentional postoperative varus alignment (3°-6°) influences TKA outcome in patients with and without preoperative varus alignment due to medial osteoarthritis of the knee. METHODS: We analyzed 172 consecutive TKA cases between April 2011 and May 2014. Patients were divided into four groups based on their preoperative and postoperative hip-knee-ankle angles (HKA): preoperative varus ≤ 3° with postoperative varus position ≤ 3° (Group 1, n = 47); preoperative varus >3° with postoperative varus ≤ 3° (Group 2, n = 104); preoperative varus ≤ 3° with postoperative varus malalignment > 3° (Group 3, n = 3); and preoperative varus > 3° with postoperative varus malalignment > 3° (Group 4, n = 18). Patients were followed up until 2 years postoperatively. RESULTS: Knee Society Score and Western Ontario and McMaster University Osteoarthritis Index scores for all study groups increased following TKA, with no postoperative differences at any time point. Group 4 performed significantly better on the Forgotten Joint Score than Group 2 (p = 0.019). Group 4 performed significantly better on the High Flexion Knee Score than Group 2 (p = 0.004) and Group 1 (p = 0.019). All other between-group differences were not statistically significant. CONCLUSION: Residual postoperative varus alignment of the lower limb does not appear to adversely affect clinical outcome following TKA for varus-type osteoarthritis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".