Accuracy evaluation of a shape-based registration method for a computer navigation system for total knee arthroplasty.
Bibliographic record
Abstract
This study evaluated the effect of computed tomography (CT) slice thickness, reconstruction pitch, intraoperative data sampling area, and data sampling volume on the accuracy of registration and determined a clinically acceptable trade-off between accuracy and surgical invasiveness. One cadaveric femur and one cadaveric tibia were used. Computed tomography of the femur and tibia were obtained using a helical scanner. Three sets of slice thickness and slice pitch were chosen for data acquisition, and two additional sets of reconstructed data were made. Bone contours were extracted by removing surrounding substrate. Surface models of bones were made from the resulting data. Registration of surface models to real objects was performed by measuring the position of various surface points on various areas of each object using an OPTOTRAK pen-probe (Northern Digital Inc, Ontario, Canada). The following trade-off is proposed as clinically optimal: perform CT with 3-mm slice thickness and 1-mm reconstruction pitch, and sample a periarticular area of 30 sampling points. The accuracy of registration in terms of position and angle was 0.8 mm and 0.6 degrees of bias with 0.2 mm and 0.3 degrees of root-mean-square in the femur, and 0.5 mm and 0.4 degrees of bias with 0.2 mm and 0.3 degrees of root-mean-square in the tibia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".