Correlation of Short Knee Radiographs and Full-length Radiographs in Patients Undergoing Total Knee Arthroplasty
Bibliographic record
Abstract
INTRODUCTION: The clinical success and longevity of a primary total knee arthroplasty (TKA) in large part depend on our ability to control coronal alignment. However, controversy exists regarding which radiographs to use for the most accurate interpretation. The study assesses the accuracy of coronal alignment measurements using a single short knee radiograph (SKR) in comparison with full-length radiographs (FLRs). METHODS: Using our institutional database, we retrieved radiographs of all patients who have had pre- and postoperative FLRs for their primary TKA in 2014. The following measurements were obtained on both short and long radiographs: femoral-tibial angle (FTA), anatomic lateral distal femoral angle, medial proximal tibial angle, condylar-plateau angle, and condylar-plateau distance. A reliability analysis was conducted between the pre- and postoperative SKRs and FLRs using the intraclass correlation coefficient (ICC). RESULTS: Radiographs of 236 limbs were included in the analysis. The FTA showed an ICC of 0.84 and 0.69 on the pre- and postoperative radiographs, respectively. Good ICC was seen in the lateral distal femoral angle in both the pre- and postoperative radiographs; these were 0.70 and 0.67, respectively. Also, the medial proximal tibial angle showed good to excellent correlation, with an ICC of 0.83 on the preoperative and 0.66 on the postoperative radiographs. CONCLUSION: This study illustrates that SKRs could be an appropriate substitute for FLRs for the evaluation of primary TKA coronal alignment, especially in the postoperative assessment of these patients. LEVEL OF EVIDENCE: 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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".