Clinical Accuracy of Imaging Techniques for Talar Neck Malunion
Bibliographic record
Abstract
OBJECTIVES: To compare the ability of plain radiographs, computed tomography (CT), and radiostereometric analysis (RSA) to detect changes in talus fracture fragment position and alignment using an in vitro model. METHODS: Eight cadaveric tali were osteotomized at the talar neck. RSA beads were inserted into each talar fragment. The talus was anatomically reduced and stabilized with a pair of 3.5-mm cortical screws. Plain radiographs and RSA films were obtained. The fragments were then displaced and rotated to create a varus and supination deformity, and screw fixation was repeated in nonanatomic alignment. Displacement and rotation were directly measured. Plain radiographs and RSA were repeated, and CT scans were obtained. The RSA measurements were interpreted in a blinded fashion by an experienced researcher. Two independent blinded orthopedic trauma surgeons measured the displacement and rotation using plain films and CT. The results from each radiographic measurement were compared to the measured displacement and rotation using ANOVA. RESULTS: Plain radiographs, RSA, and CT all underestimated the measured talar neck displacement and rotation. Radiographs underestimated displacement by 5.0 +/- 2.9 mm, RSA by 5.9 +/- 2.0 mm, and CT scans by 2.4 +/- 4.8 mm (P < 0.05). Rotation was also underestimated by all 3 techniques, but the differences among techniques were not statistically significant. CONCLUSIONS: The most accurate imaging technique to measure displacement in talar neck malunion is CT scan. RSA was less useful as an imaging technique in this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".