Small Random Angular Variations in Pelvic Tilt and Lower Extremity Can Cause Error in Static Image-based Preoperative Hip Arthroplasty Planning: A Computer Modeling Study
Bibliographic record
Abstract
BACKGROUND: Many THA simulation models rely on a limited set of preoperative static radiographs to replicate sagittal pelvic tilt during functional positions and to recommend an implant orientation that minimizes the risk of prosthetic impingement. However, possible random changes in pelvic or lower extremity angular motions and the effect of coronal and axial pelvic tilt are not included in these preoperative models. QUESTIONS/PURPOSES: (1) Can prosthetic impingement occur if the pelvic tilt or lower extremity alignment randomly varies up to ± 5° from what is measured on a single preoperative static radiographic image? (2) Do changes in coronal and axial pelvic tilt or lower extremity alignment angles have a similar effect on the risk of prosthetic impingement? METHODS: A de-identified pelvis and lower-body CT image of a male patient without previous THA or lower extremity surgery was used to import the pelvis, femur, and tibia into a verified MATLAB computer model. The motions of standing, pivoting, sitting, sit-to-stand, squatting, and bending forward were simulated. THA implant components included a full hemispherical acetabular cup without an elevated rim, polyethylene liner without an elevated rim, femoral head (diameter: 28 mm, 32 mm, 36 mm, or 40 mm), and a triple-taper cementless stem with three different neck shaft angles (127°, 132°, or 135°) with a trapezoidal neck were used in this model. A static model (cup anatomical abduction 40°, cup anatomical anteversion 20°, stem anatomical anteversion 10°) with a predefined range of sagittal pelvic tilt and hip alignment (0° coronal or axial tilt, without random ± 5° change) was used to simulate each motion. We then randomly varied pelvic tilt in three different pelvic planes and hip alignments (flexion, extension, abduction, adduction, rotation) up to ± 5° and assessed the same motions without changing the implant's anatomical orientation. Prosthetic impingement as the endpoint was defined as mechanical abutment between the prosthetic neck and polyethylene liner. Multiple logistic regression was used to investigate the effect of variation in pelvic tilt and hip alignment (predictors) on prosthetic impingement (primary outcome). RESULTS: The static-based model without the random variation did not result in any prosthetic impingement under any conditions. However, with up to ± 5° of random variation in the pelvic tilt and hip alignment angles, prosthetic impingement occurred in pivoting (18 possible combinations), sit-to-stand (106 possible combinations), and squatting (one possible combination) when a 28-mm or a 32-mm head was used. Variation in sagittal tilt (odds ratio 4.09 [95% CI 3.11 to 5.37]; p < 0.001), axial tilt (OR 3.87 [95% CI 2.96 to 5.07]; p < 0.001), and coronal tilt (OR 2.39 [95% CI 2.03 to 2.83]; p < 0.001) affected the risk of prosthetic impingement. Variation in hip flexion had a strong impact on the risk of prosthetic impingement (OR 4.11 [95% CI 3.38 to 4.99]; p < 0.001). CONCLUSION: The combined effect of 2° to 3° of change in multiple pelvic tilt or hip alignment angles relative to what is measured on a single static radiographic image can result in prosthetic impingement. Relying on a few preoperative static radiographic images to minimize the risk of prosthetic impingement, without including femoral implant orientation, axial and coronal pelvic tilt, and random angular variation in pelvis and lower extremity alignment, may not be adequate and may fail to predict prosthetic impingement-free ROM. CLINICAL RELEVANCE: Determining a safe zone for THA implant positioning with respect to impingement may require a dynamic computer simulation model to fully capture the range of possible impingement conditions. Future work should concentrate on devising simple and easily available methods for dynamic motion analysis instead of using a few static radiographs for preoperative planning.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".