Revising Substantial Leg Length Discrepancy in Total Hip Arthroplasty Using Computer-assisted Navigated Systems: A Case Series of Three Patients
Bibliographic record
Abstract
One of the primary challenges of total hip arthroplasty (THA) is equalizing the limb lengths to re-establishing normal hip biomechanics. Post-operative leg length discrepancies (LLD) lead to patient dissatisfaction and are a main source of orthopedic malpractice cases. The aim of this case series was to document three cases of substantial LLD that were corrected during THA with the assistance of an imageless computer navigation system. Medical records were reviewed for history and radiographs were consulted. All patients in this series presented with complaints related to hip fractures and reported a significant lengthening of leg length following THA. No surgical complications of adverse events were reported. In these cases, imageless navigation provided intraoperative measurements of leg length which allowed for enhanced accuracy of component placement and improved outcomes following surgery.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".