3D - Printed Patient Specific Instrumentation in Corrective Osteotomy of the Femur and Pelvis: A Review of the Literature
Bibliographic record
Abstract
BACKGROUND: The paediatric patient population has considerable variation in anatomy. The use of Computed Tomography (CT)-based digital models to design three-dimensionally printed patient specific instrumentation (PSI) has recently been applied for correction of deformity in orthopedic surgery. This review sought to determine the existing application of this technology currently in use within paediatric orthopaedics, and assess the potential benefits that this may provide to patients and surgeons. METHODS: A review was performed of MEDLINE, EMBASE, and CENTRAL for published literature, as well as Web of Science and clinicaltrials.gov for grey literature. The search strategy revolved around the research question: "What is the clinical impact of using 3D printed PSI for proximal femoral or pelvic osteotomy in paediatric orthopaedics?" Two reviewers, using predetermined inclusion criteria, independently performed title and abstract review in order to select articles for full text review. Data extracted included effect on operating time and intraoperative image use, as well as osteotomy and screw positioning accuracy. Data were combined in a narrative synthesis; meta-analysis was not performed given the diversity of study designs and interventions. RESULTS: In total, ten studies were included: six case control studies, three case series and a case report. Five studies directly compared operating time using PSI to conventional techniques, with two showing a significant decrease in the number of intraoperative images and operative time. Eight studies reported improved accuracy in executing the surgical plan compared to conventional methods. CONCLUSION: Compared to conventional methods of performing femoral or pelvic osteotomy, use of PSI has led to improved accuracy and precision, decreased procedure times, and decreased intra-operative imaging requirements. Additionally, the technology has become more cost effective and accessible since its initial inception and use.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".