Computed Tomography-Based Navigation for Hip, Knee, and Spine Surgery
Bibliographic record
Abstract
A review of CT-based orthopaedic navigation is presented with a specific emphasis on arthroplasty for the hip and the knee. Fundamental issues about the laboratory and clinical validation of the applications are addressed. The ability to compute the position and orientation of an acetabular implant using a postoperative CT scan was investigated. Angle deviations relative to known positions were computed with an error of less than 1 degree. Then, the system accuracy for three-dimensional reconstruction and registration of two cadaveric pelvis specimens was measured with more than 350 registrations. We observed a maximal inclination error of 5 degrees in 99% of cases and a maximal anteversion error of 5 degrees in 97% of cases. The accuracy of the three-dimensional reconstruction and registration for knee arthroplasty also was measured and computed with an angular accuracy of 0.5 degrees in the AP plane and accuracy of 3 degrees in the lateral plane. A clinical study then was done in 109 cases where 96% of implants were installed with a hip-knee-ankle angle of 180 +/- 3 degrees . Computed tomography-based navigation for orthopaedic surgery provides greater accuracy and reproducibility than conventional surgery. As noted by learning curves, software improvements are needed to bring it into daily clinical routine.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".