Use of patient-specific guides and 3D model in scapula osteotomy for symptomatic malunion
Bibliographic record
Abstract
BACKGROUND: Scapular osteotomy for malunion can lead to resolution of pain and functional improvement in scapula fracture sequelae. Understanding three-dimensional bone morphology and analysing post-traumatic deformity is the main step of planning and the key to success of the procedure. 3D models and patient-specific guides are a growing technology to enhance accuracy of planning and to assist during surgery. CASE PRESENTATION: We report the case of a 50 years old male, complaining of pain and limited function after a malunited scapular body fracture. Clinical assessment showed a severe impairment of shoulder function with active and passive forward flexion limited to 80°, absent external rotation, and internal rotation limited to the buttock. X-rays and CT scan showed an excessive lateral border offset of 53 mm and complete displacement of the glenoid segment anteriorly and medially to the scapular body, with impingement between the lateral most prominent scapular bone spur and humeral shaft. Glenopolar angle was 19°, scapular body angulation on the sagittal plane was 12°. Corrective osteotomy was planned on a virtual interactive rendering and on 3D printed models. Patient-specific guides were developed to perform a body-spine osteotomy with removal of a bone wedge, and a glenoid-spine osteotomy; a patient-specific wedge spacer was used to hold the reduction during plate fixation. Follow-up up to 12 months after surgery demonstrated improvement in scapula anatomy, shoulder girdle function, and patient-reported outcomes. CONCLUSIONS: For the first time in scapula malunion surgery, patient-specific osteotomy guides were succesfully used during surgery to perform osteotomies and to assist in reduction maneuvers.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".