Global varus malalignment increase from double‐leg to single‐leg stance due to intra‐articular changes
Bibliographic record
Abstract
Abstract Purpose Preoperatively planned correction for tibial osteotomy surgery is usually based on weightbearing long‐leg Xrays, while the surgery is performed in a supine non‐weightbearing position. The purpose of this study was to assess the differences in lower limb alignment in three different weightbearing conditions: supine position, double‐leg (DL) stance and single‐sleg (SL) stance prior to performing a medial opening wedge high tibial osteotomy (MOWHTO) for varus malalignment. The hypothesis of this study was that progressive limb‐loading would lead to an increased preoperative varus deformity. Material and methods This retrospective study included 89 patients (96 knees) with isolated medial knee osteoarthritis (Ahlbäck grade I or II) and significant metaphyseal tibial vara (> 6°). The differences between supine position, DL stance and SL stance were analysed for the hip‐knee‐ankle angle (HKA), lateral distal femoral angle (LDFA), medial proximal tibial angle (MPTA), weight‐bearing line ratio (WBL) and joint line convergence angle (JLCA). Results From a supine position to DL stance, the HKA angle slightly increased from 175.5° ± 1.1° to 176.3° ± 1.1° and JLCA changed from 2.0° ± 0.3° to 1.8° ± 0.3° without a statistically significant difference. From DL to SL stances, the HKA angle decreased from 176.3° ± 1.1° to 174.4° ± 1.1° (p < 0.05) and the JLCA increased from 1.8° ± 0.3° to 2.6° ± 0.3° (p < 0.05). A significant correlation was found between ΔHKA and ΔJLCA between the DL and the SL stances (R2 = 0.46; p = 0.01). Conclusion Varus malalignment increases with weight‐bearing loading from double‐leg to single‐leg stances with an associated JLCA increase. Thus, single‐leg stance radiographs may be useful to correct preoperative planning considering patient‐specific changes in JLCA. Level of clinical evidence III, retrospective comparative study.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".