Specific case consideration for implanting TKA with the Kinematic Alignment technique
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The Kinematic Alignment (KA) technique for total knee arthroplasty (TKA) is an alternative surgical technique aiming to resurface knee articular surfaces. The restricted KA (rKA) technique for TKA applies boundaries to the KA technique in order to avoid reproducing extreme constitutional limb/knee anatomies. The vast majority of TKA cases are straightforward and can be performed with KA in a standard (unrestricted) fashion. There are some specific situations where performing KA TKA may be more challenging (complex KA TKA cases) and surgical technique adaptations should be included. To secure good clinical outcomes, complex KA TKA cases must be preoperatively recognized, and planned accordingly. The proposed classification system describes six specific issues that must be considered when aiming for a KA TKA implantation. Specific recommendations for each situation type should improve the reliability of the prosthetic implantation to the benefit of the patient. The proposed classification system could contribute to the adoption of a common language within our orthopaedic community that would ease inter-surgeon communication and could benefit the teaching of the KA technique. This proposed classification system is not exhaustive and will certainly be improved over time. Cite this article: EFORT Open Rev 2021;6:881-891. DOI: 10.1302/2058-5241.6.210042
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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 it