Three-Dimensional Preoperative Planning of Corrective Osteotomies for Distal Radius Malunions: A Systematic Review of Clinical and Radiographic Outcomes
Bibliographic record
Abstract
Distal radius malunions (DRMs) are painful and functionally impairing, often necessitating surgical intervention to restore wrist anatomy and function. Traditional corrective osteotomies, which rely on orthogonal radiographs, may overlook complex deformities. This study aims to evaluate the techniques and effectiveness of 3-dimensional (3D)-planned corrective osteotomies, along with their clinical and radiographic outcomes. We conducted a systematic review of literature across PubMed, Ovid, EMBASE, and Web of Science for studies that implemented 3D planning in corrective osteotomies for DRM. We identified 792 articles, of which 24 met the inclusion criteria with a total of 199 corrective osteotomies of symptomatic DRM, of which 127 (64%) were extra-articular and 39 (19.5%) intra-articular, with the remaining being a combination of intra-articular and extra-articular or unspecified. To transfer 3D preoperative plan to patient, 18 out of 24 used 3D-printed patient-specific cutting guides for intraoperative guidance. Two studies implemented the transfer of the preoperative plan using simulated osteotomies on 3D-printed models, while one study used a dynamic referencing body to match real-time surgical actions with the virtual plan. The majority (98.5%, n = 196) demonstrated statistical significance in achieving the acceptable limits of radial inclination (21°-25°), ulnar variance (<3 mm), and volar tilt (≤15° dorsal and ≤20° volar). Functional outcomes significantly improved in all studies ( P < .05). Complications were reported in 22 cases (11%) and included partial laceration of the extensor pollicis longus tendon, hardware problems requiring removal, and screw loosening. Future research should focus on balancing the technique’s additional costs and logistical demands with its potential long-term benefits.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".