Long-term outcome of two-incision total hip arthroplasty with intraoperative imageless navigation for cup placement: A concise followup, at ten to twelve years, of a previous report
Bibliographic record
Abstract
Background: We previously reported at a minimum follow-up of one-year results of the use of imageless navigation for cup placement in primary two-incision total hip arthroplasties (THA). The purpose of the present report is to update that study and report the ten to twelve-year outcomes. Materials and methods: Twelve consecutive patients underwent minimally invasive two-incision (MIS-2) THA done by a single experienced surgeon between September 2003 and January 2005. A retrospective chart review was performed to investigate clinical assessment (the Harris hip score [HHS] and the Western Ontario and McMaster University Osteoarthritis Index [WOMAC] scale), radiographic analysis, the complications and survivorship. Results: At the latest follow-up evaluation, the HHS and WOMAC scale were 95.4 and 96.5 points respectively. There were no radiographic evidence of definite loosening. Injury to the lateral femoral cutaneous nerve occurred in 4 hips. There were no dislocation, no periprosthetic joint infection or fracture. The 10- year Kaplan-Meier analysis revealed 100% survival rate with revision for any reason as the end point. Conclusion: This study demonstrated that imageless navigation system for the two-incision THA give excellent long-term results.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".