Correlation of tibial bone defect shape with patient demographics following total knee revision
Bibliographic record
Abstract
BACKGROUND: Bone defects of the proximal tibia following revision total knee arthroplasty (TKA) are challenging to manage, but must be addressed to provide lasting stability. This paper will categorize tibial bone defects into shape groups and correlate resulting groups to patient demographic data. METHODS: Retrospective analysis of four hundred and four patients post revision TKA between January 2005 and February 2014 was conducted. One hundred and eighteen met the inclusion criteria and were subcategorized by defect shape on their post-operative lateral and anterior-posterior (AP) radiographs. The subgroups of defect shape were subsequently analyzed with Fisher's exact test and one way ANOVA. RESULTS: Trapezoidal shaped defects were the most common in both radiographic views, and the magnitude of the defect at the top joint line varied significantly amongst shape groups in both AP and lateral views. Trapezoid shaped defects were correlated with smaller defect top lengths in both views. There was no statistical correlation between defect shape BMI, TIV and reason for revision in lateral view. However, T-bilateral defect shapes were correlated with higher BMIs in AP view. CONCLUSION: A volumetric classification system of tibial defects is necessary for preoperative planning in revision TKA. Common tibial bone defect shape groups were identified and analyzed in AP and lateral radiographs after revision TKA. Trapezoidal defects were the most common, and all other shapes followed a pattern of proximal enlargement tapering distally. Trapezoidal defects were smaller than other shapes and AP T-bilateral shaped defects were correlated with higher BMIs.
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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 | 0.005 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".