Stricter correction of lower extremity length is required during total hip arthroplasty in patients with ankylosing spondylitis
Bibliographic record
Abstract
Abstract Background: Patients with ankylosing spondylitis often have fusions in the spine and sacroiliac joints, such that it is difficult to compensate for the difference in length of the lower extremities. Methods: We retrospectively measured the difference in lower extremity length after total hip arthroplasty(THA) in 89 patients with ankylosing spondylitis from June 2004 to February 2021 at Our institute. Patients were divided into two groups based on a difference in lower extremity length of 5 mm or 10 mm. Clinical outcomes were investigated using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) and Harris Hip Score (HHS). In addition, these points are investigated : patient satisfaction with the operation; whether there was a current difference in leg length; and whether there was a limping gait. Results: The group with a leg length difference of 5–10 mm rather than <5 mm had significantly worse WOMAC pain and stiffness. The survey revealed statistically significant differences in patient satisfaction with the operation, limping gait, and whether back pain had improved. Conclusion: For patients with ankylosing spondylitis, reducing the difference in length of the lower extremities to <5 mm, which is more accurate than the current standard of <10 mm, may produce greater improvement in clinical outcomes after hip arthroplasty.
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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 | 0.003 |
| 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) | 0.001 | 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".