Computer-enhanced insertion of the Oxford Unicompartmental arthroplasty: a Fluoroguide technique
Bibliographic record
Abstract
Objective: To develop an accurate and reproducible technique for inserting Oxford Unicompartmental arthroplasties.Materials and Methods: A protocol was developed to accurately position the femoral component of the Oxford Unicompartmental arthroplasty using computer-enhanced techniques. A dynamic reference body (DRB) is inserted into the femur, then antero-posterior (AP) and lateral fluoroscopic images are taken of the proximal femur. These images are digitized and entered into 3D space using a registered fluoroscopy machine. The femoral guide from the Oxford system is then placed on the medial femoral condyle in the standard manner. Holes are drilled directly towards the center of the femoral head using a registered drill. The femoral cuts are then made using the standard instruments from the Oxford set.Results: This procedure was carried out on 15 sawbone femurs. Component position was determined by measuring radiographs after a trial component had been inserted. The average lateral error was 1.2° (standard deviation [SD] = 1.207), compared to 4.1° (SD = 2.875) with the standard technique. The average AP error was 2.5° (SD = 1.767), compared to 5.1° (SD = 2.219) with the standard technique. The differences in accuracy in both planes were statistically significant (p = 0.002 for AP, 0.001 for lateral).Conclusion: Using a Fluoroguide-assisted technique, we were able to insert the femoral component more precisely than when using the standard technique as described by the manufacturer.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
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 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".