Improving radiographic patello‐femoral tracking in total knee arthroplasty with the use of a flexion spacer: a case–control study
Bibliographic record
Abstract
Abstract Purpose The purpose of this study was to compare the effectiveness of a flexion spacer in the clinical and radiological outcomes of patients who underwent total knee arthroplasty (TKA) and to compare these patients to a group of patients subjected to the same type of surgery but without the use of a flexion spacer. It was hypothesized that patients who underwent TKA using a flexion spacer would have better clinical and radiological outcomes than those without a flexion spacer in both short‐ and medium‐term follow‐ups. Methods A consecutive series of patients undergoing TKA were included, yielding 20 patients in the study group. The control group was identified from the consultant database of the senior author, yielding 21 patients who underwent the same operation. All 41 patients received a Vanguard Knee System (Zimmer‐Biomet, Warsaw, Indiana, USA). Cases were defined as those patients who had undergone TKA using a flexion spacer device for gap balancing; controls were defined as patients who had undergone TKA without the support of a flexion spacer device. Patients were clinically and radiographically evaluated at two consecutive follow‐ups: T1—13.1 ± 1.3 months and T2—108 ± 6 months. Clinical evaluation was performed using the Knee Society Scoring System and the Western Ontario, McMaster Universities Osteoarthritis Index score. Radiographic evaluation included the femoral angle (α), the tibial angle (β), the sagittal femoral (γ) angle and the tibial slope (δ). Furthermore, the lateral patellofemoral angle (LPFA) and the Caton‐Deschamps index were evaluated. Results No statistically significant clinical differences were found between the two groups at T1 and T2; moreover, the clinical outcomes of the two groups were stable between the two follow‐ups, with no significant improvement or worsening. Radiographic evaluation showed no difference in the two groups between T1 and T2; the only significant radiographic difference between the two groups concerned the LPFA (both at 30° and 60°) at each follow‐up, which was significantly greater in cases than in controls (p = 0.001). Conclusions The current study demonstrates that the use of a flexion spacer significantly improves radiographic patello‐femoral tracking, although no significant clinical differences were found between the two groups. Level of evidence Case–control study, level III.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".