Impact of Offset and Leg Length on Functional Outcomes Post-Total Hip Arthroplasty: How Accurate Should Coronal Reconstruction Be?
Bibliographic record
Abstract
BACKGROUND: Accurate hip reconstruction is associated with improved biomechanical behavior following total hip arthroplasty (THA). However, whether this is associated with improved patient-reported outcomes (PROs) is unknown. HYPOTHESIS/PURPOSE: This study aimed to: 1) describe the ability to reconstruct coronal geometry during THA without advanced technology; 2) assess whether restoration of global offset (GO) and leg length (LL) is associated with improved PROs; and 3) investigate whether increased femoral offset (FO) to compensate for reduced acetabular offset (AO) influences PROs. METHOD: This was a prospective, multicenter, consecutive cohort study of 500 patients treated with primary THA without robotics or navigation. The Oxford Hip score (OHS) was obtained preoperatively and at 1-year follow-up. Supine anteroposterior pelvic radiographs were analyzed to determine AO, FO, GO, and LL relative to the native contralateral side. Contour plots for ΔOHS based on ΔLL and ΔGO were created, and ΔOHS was calculated within and outside various ranges (±2.5, ±5, or ±10 mm). RESULTS: In the operated hip, mean FO increased by 3 ± 6 mm (range, -16 to 27), while AO decreased by 2 ± 4 mm (range, -17 to 10). The contour graph for ±2.5 mm zones showed the best outcomes (ΔOHS >25) with GO and LL centered on 0 ± 2.5 mm (P < .01). However, only 10% achieved such reconstruction. When GO and LL differences were within ±10 mm, ΔOHS was superior when both AO and FO were within ±5 mm (mean: 24 ± 10; range, -5 to 40) compared with when FO was above 5 mm to compensate for a reduction in AO (mean: 22 ± 11; range, -10 to 46; P = .040). CONCLUSIONS: The PROs were associated with biomechanical reconstruction, and the best clinical improvement can be expected when GO and LL differences are both within 2.5 mm. Maintenance of AO is important, as compensation by increasing FO is associated with inferior OHS.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".