Percutaneously assisted total hip (PATH) and Supercapsular percutaneously assisted total hip (SuperPATH) arthroplasty: learning curves and early outcomes.
Bibliographic record
Abstract
BACKGROUND: A new family of micro-posterior approaches, percutaneously assisted total hip (PATH), SuperCapsular (SuperCap) and Supercapsular percutaneously assisted total hip (SuperPATH) allow preservation of the short external rotators. This study assesses early outcomes and learning curves of the PATH and SuperPATH approaches. METHODS: Early outcomes of the first consecutive 49 PATH and 50 SuperPATH cases performed by a non-developer surgeon were evaluated. Analysis of variance (ANOVA) was used to compare age, body mass index (BMI), and pre-operative hemoglobin. Gender was compared using a Chi-square test. Clinical outcomes were compared using a nonparametric Wilcoxon test or a Chi-square test. Learning curves were assessed using operative time as a surrogate. Acetabular cup abduction and anteversion were compared using the first post-operative radiograph and a modified protractor. RESULTS: Both cohorts were similar with respect to diagnosis, gender, and BMI. Mean operative time in minutes was recorded for the PATH (114.5±17.5) and SuperPATH (101.7±18.3) cohorts (P value =0.0002). PATH operative time reached a plateau by case 40, but SuperPATH operative time continued to decrease by case 50. Transfusion rates were low in the PATH (4%) and SuperPATH (6%) cohorts. Mean length of stay (LOS) in days for the SuperPATH and PATH cohorts were 2.2 and 3.0, respectively (P value <0.0001). Complication rates were low in the SuperPATH (4.0%) and PATH (4.1%) cohorts. Acetabular cups in the SuperPATH cohort (anteversion: 23.5°±8.2°, abduction: 39.0°±8.4°) were significantly more anteverted (P value <0.0001) and less abducted (P value <0.05) than in the PATH cohort (anteversion: 13.1°±7.1°; abduction: 42.9°±7.6°). CONCLUSIONS: Early results demonstrate that the PATH and SuperPATH approaches can be adopted with minimal complications and outcomes consistent with innovator outcomes, even during the learning curve. The SuperPATH technique was associated with shorter operative time that continued to decrease, suggesting that proficiency continues to decrease beyond the first 50 cases. In this author's experience, acetabular cups implanted using the SuperPATH technique were more anteverted than those implanted using the PATH technique. Greater use of the transverse acetabular ligament to guide cup alignment reduced this effect.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".