Bibliographic record
Abstract
In this case series, patient-specific guides (PSG) based on 3D reconstruction and printed PSG were designed to assist in locating the optimal placement for an acetabular cup in THR. The study aims to determine whether PSG can help orientate an ideal acetabulum in anatomical deranged acetabula during THR. We hypothesize that implementing 3D preoperative plans using PSG will most accurately approximate the acetabulum for acetabular cup implantation. The pelvises of four patients were imaged and reconstructed with computed tomography (CT). The angle of lateral opening (ALO), angle of inclination and version were calculated by means of computer-aided (CAD) image analysis. PSG was 3D printed matching the acetabular orientation preoperatively planned and used to direct acetabular reaming tools. Acetabular cup alignment and implantation were performed with PSG. The coefficient of variation was measured by comparing the pre-and postoperative values measured from CT. Preoperatively planned cup orientation closely measured final cup placement. In this study, 3D-printed acetabular PSG has shown promising results in precisely matching the surface and the unique anatomy of the distorted acetabula. Without the use of hip positioning guides set at reference values for ALO and version angles, we concluded that using the guides produced from the patient’s specific anatomy, provided the optimal result for maintaining the rotation centre within the acetabulum. While PSG offers advantages in accurately recreating hip anatomy and optimizing implant placement, challenges such as the learning curve and increased costs must be considered. Publication History Article published online: 15 July 2025 © 2025. Thieme. All rights reserved. Georg Thieme Verlag KG Oswald-Hesse-Straße 50, 70469 Stuttgart, Germany
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".