The contribution of lower limbs to Pelvic Tilt: A baseline and postoperative full-body analysis
Bibliographic record
Abstract
BACKGROUND: Pelvic tilt (PT) has been a parameter of interest in biomechanics of spinal deformity for decades. It remains unclear how patients achieve different values of PT pre- and postoperatively. RESEARCH QUESTION: This study aimed at assessing the relative contribution of hip extension, knee flexion and ankle extension to PT, factoring malalignment and hip osteoarthritis (OA). METHODS: This retrospective study included Adult Spinal Deformity (ASD) patients with preoperative full-body radiographs from a multicenter database, with a sub-analysis of patients with complete 1-year follow-up (1yFU). Age and PI-adjusted normative PT (NormPT) and offset from norm (OffPT) were calculated, as for sacro-femoral angle (SFA), knee flexion angle (KA) and ankle angle (AA). Multivariate linear regression models controlling for age, frailty, severe hip OA, pelvic incidence (PI), SFA, and KA were used to predict PT at baseline, and offset from NormPT. Another model was generated to predict PT change. RESULTS: 600 patients at baseline and 336 with 1yFU were included. Mean age was 61 ± 15, 70.2 % were females and 40 % were revision cases. At baseline, regression analysis revealed that 0.9° increase in hip extension (SFA) and a 0.6° increase in knee flexion (KA) amounted to 1° increase in PT. Knee and ankle contribution to PT significantly increased for while hip extension decreased as TPA augmented (p < 0.001). Patients with low deformity compensated with hip extension, while knee flexion was the largest contributor of PT in high deformity patients: 70.7 % (44.7 - 111.9). Median contribution of knee flexion to PT was larger for patients who presented hip OA. SIGNIFICANCE: This study demonstrated that PT is a phenomenon driven by extension of the hips and flexion of the knees and proposed values to predict PT from those two compensatory mechanisms. Magnitude of spinal deformity and hip OA alters the magnitude of SFA/KA contribution to PT.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".