Fixation-based surgery: A new technique for distal radius osteotomy
Bibliographic record
Abstract
OBJECTIVE: The objective was to develop a fixation-based three-dimensional presurgical planner and an intraoperative guidance system for distal radius osteotomy. Fixation-based surgery is a technique premised on using a fixation device, such as a fracture-fixation plate, during the alignment and distraction phases of an osteotomy. MATERIALS AND METHODS: The planning system and guidance system were coded using OpenGL on UNIX workstations. In vitro tests were performed to compare the reproducibility of the computer-enhanced technique to that of the traditional technique, and an in vivo pilot study was initiated. RESULTS: In vitro, the computer-enhanced technique produced a significant reduction by more than one half in both the maximum error of correction and the standard deviation of the correction error. Preliminary in vivo results on six patients suggest that similar error diminution will occur during regular clinical application of the technique. CONCLUSIONS: Both studies showed that the computer system is simple to use. The planning system allowed the surgeon to perform multiple simulations of the surgical procedure preoperatively, which were used to optimize the plan and identify potential problems during realignment. The use of a fixation-based technique avoided the complexity of attempting to guide the surgeon to realign a bone fragment in six degrees of freedom of correction, and eliminated the use of X-ray fluoroscopy for achieving the alignment.
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 | 0.000 | 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 | 0.000 |
| 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".