True Confessions of Neutral Mechanical Disciple—How I Learned to Love a Patient-Specific Target
Bibliographic record
Abstract
INTRODUCTION: Classical neutral mechanical alignment in total knee arthroplasty (TKA) has been a standard paradigm, while more recently, other alignment schemas, such as kinematic, individualized, and functional, have been explored. This study aimed to investigate the effect of three-dimensional (3D) computed tomography (CT)-based surgical robotics inputs on a classically trained surgeon's TKA component positions and alignment targets over time. MATERIALS AND METHODS: Data from 1,394 consecutive robotically-assisted TKAs by a single surgeon from 2016 to 2020 were analyzed. Metrics collected included pre-balance planned implant component positions, final planned implant component positions after soft tissue balancing, and constitutional alignment from CT scans. Joint line obliquity was plotted against the arithmetic hip-knee angle (aHKA) using coronal plane alignment of the knee (CPAK). Three categories of alignment strategy were defined: true mechanical alignment (tMA), adjusted mechanical alignment (aMA), and no mechanical alignment (noMA). RESULTS: A shift to overall varus component positioning was observed over the years. Joint line obliquity according to CPAK showed a wider spread in later years, and the distribution of tibial and femoral coronal alignment angles expanded over time. CONCLUSION: The study revealed a change in alignment targets and final positioning of components away from neutral biomechanical axes in a large volume of TKAs by a single, classically trained surgeon over five years of using a robotic arm-assisted TKA system with CT-based planning. The most dominant factor for this change was the use of 3D CT planning, allowing the surgeon to assess patient-specific anatomy and plan accordingly. Outcome data is needed to determine if this change in behavior and surgical technique was beneficial. In summary, using a CT scan-based robotically assisted technique led to a gradual and complete shift from tMA to predominantly a non-mechanically aligned philosophy in TKA.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".