CT (ISO-C-3D) image based computer assisted navigation in trauma surgery: A preliminary report
Bibliographic record
Abstract
CT (ISO-C-3D) image based navigation has recently been introduced to improve the image quality and accuracy during computer assisted orthopaedic surgeries. We report on our early experience using this novel technique in intra-articular lower extremity fracture management. Real time CT-based navigation assisted surgery was used in the treatment of five patients with fractures of tibial plateau (3), talus (1) and acetabulum (1). The mean age was 38 years (range, 29–47). Feasibility, pitfalls and adequacy of reduction and fixation were evaluated. Additional time spent before the surgical incision (Δ time) using the ISO-C navigation and total operative time was measured. All five procedures were regarded as technically successful. Accurate reduction and fixation of all the fractures was achieved. All the fractures were fixed with closed reduction and internal cannulated screw fixation. Mean additional time spent after the start of anaesthesia and until surgical incision for cannulated screw insertion (Δ time) was 26 min. The average total operative time was 109 min. Combining the ISO-C-3D images with computer navigation can improve the safety and decrease the invasiveness of the procedures in trauma surgery. 3D navigation makes the reduction and screw placement highly accurate but may extend the operative time.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".