DOES REPLICATION OF NATIVE ACETABULAR ANATOMY SATISFY HIP-SPINE ALGORITHM PLANS FOR CUP ORIENTATION?
Bibliographic record
Abstract
Patient-specific planning algorithms accounting for the hip-spine interaction are available for surgeons to use to minimize impingement- and dislocation- risk. Whether replicating native anatomy, such as the Transverse-Acetabular-Ligament (TAL), leads to satisfactory cup orientation is unknown. This study aims to assess whether replication of native anatomy, particularly TAL, would lead to cup orientation in line with hip-spine recommendations in patients, with and without adverse spinopelvic characteristics. 100 patients, pre-THA, that underwent detailed spinopelvic assessment [pre-op CT pelvis/femurs, spinopelvic radiographs (supine, standing and deep-seated)] was studied. The cohort comprised of 50 patients without any adverse spinopelvic characteristics and 50 patients with adverse characteristics [spinopelvic imbalance (PI-LL>20) & high PTstanding (>19°) & and lumbar stiffness (segmented to determine native acetabular anatomy, including TAL version, relative to the horizontal (Functional-version) and Anterior-Pelvic-Plane (Morphological-version). Native anatomy was compared to pre-operative plans as per 1. Patient-Specific-Instrumentation (PSI) planning considering impingement and edge-loading algorithms-; 2) Optimum-Combined-Sagittal-Index (CSIstanding: 205–245°). Native morphological anatomy was similar between groups for inclination (51°±8 vs. 50°±8; p=0.4) and version (19°±6 vs. 18°±8; p=0.7). However, functional version was different (15°±6 vs. 21°±7; p higher by 12°±5 (p non-adverse group (7°±7). Aiming for replication of TAL version and 40° radiographic inclination, satisfied CSIstanding in 98% of cases for both groups. Replication of TAL version and radiographic inclination of 40° would lead to satisfactory orientation in almost all patients, including those with adverse spinopelvic characteristics. Tools to improve accuracy and precision (consistency in achieving target) are necessary as visual aids such as TAL are not always clearly visible and might be erroneously interpreted intra-operatively.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".