Online Instruction to Measure Axial Alignment with the Bonesetter App
Bibliographic record
Abstract
Background and objectives: Alignment of the lower extremity is important when treating congenital deformities, fractures, and joint replacement. During the COVID-19 pandemic, AO North America offered an online course on deformity measurement and planning. The Bonesetter app is a deformity planning tool that is freely available online. The purpose of this study was to see how effective an online course was in teaching axial alignment measurement and to assess that skill using an online digital planning tool, the Bonesetter app. Materials and Methods: An online module on axial alignment was provided during the AONA osteotomy course as well as a tutorial on how to use an online digital planning tool (Bonesetter app). The tools within the Bonesetter app allow users to draw digital lines directly on the CT images and measure the exact angle between two planes. Participants in this study were directed to perform these measurements in four different cases that tested different variations of deformity. Results: The measurements were completed correctly in case 1 = 56%, case 2 = 61%, case 3 = 84%, and case 4 = 76%. The standard deviation of angular digital measurements between individuals was ±4.26 degrees. Measuring the angle directly vs. drawing angles to a horizontal line had smaller standard deviations per case (p < 0.005) and less incidents outside 1 standard deviation for each measurement. Errors in adding and subtracting were the most common errors, particularly in relation to femoral anteversion or retroversion. Conclusions: The online course successfully instructed a group of orthopedic surgeons to measure alignment and malalignment of lower limb axial deformities. The Bonesetter app helped participants to learn this skill and identify errors in measurement. The inability to differentiate between anteversion and retroversion of the femur is a common source of error when it occurs and should be a focus of instruction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".