The Kingston periacetabular osteotomy utilizing computer enhancement: A new technique
Bibliographic record
Abstract
OBJECTIVE: To develop a new periacetabular osteotomy technique that can be performed safely and reliably using computer-enhanced technology. MATERIALS AND METHODS: This technique uses a modified posterior approach with a trochanteric osteotomy. A 3D surface model is generated from CT data. The osteotomy is planned using custom software developed by our team. A dynamic reference body is fixed to the iliac crest and the pelvis is registered using an optically tracked probe (Optotrak, Northern Digital, Ontario, Canada). A tracked probe is used to mark the osteotomies in three dimensions. The posterior column is osteotomized between the sciatic notch and hip joint. The pubic ramus is osteotomized under fluoroscopic guidance. The acetabular fragment is rotated into a more appropriate position and fixed with pelvic reconstruction plates. Subjective and objective data are collected pre- and postoperatively. RESULTS: This procedure has been performed on eight patients. Average center-edge angle correction has been 17 degrees. The computer and optical guidance system has provided accurate information in seven of eight cases, and there have been no complications. CONCLUSIONS: This technique has enabled us to perform periacetabular osteotomies with safety and predictability. Using this computer-enhanced technique, periacetabular osteotomy may become a more common procedure in the practice of hip reconstruction surgeons.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".