Evaluating the learning curve and outcomes of a new rectangular femoral stem in total hip arthroplasty: A comparative study
Bibliographic record
Abstract
Total hip arthroplasty (THA) is a widely successful procedure, but the adoption of new femoral stems is often met with hesitation due to concerns regarding a learning curve and potential complications. This study evaluates the impact of introducing a new rectangular femoral stem by comparing radiographic, clinical, and functional outcomes with those of an established metaphyseal loading stem. A retrospective comparative study was conducted between January 2022 and January 2024. Patients were categorized into three groups: (1) control group receiving an established metaphyseal loading stem, (2) “learning curve” group (first half of patients receiving the new rectangular stem), and (3) “experienced” group (second half of patients receiving the rectangular stem). Primary outcomes included femoral stem subsidence and diaphyseal canal filling. Secondary outcomes comprised Oxford Hip Scores (OHS), EQ-5D-5L scores, length of hospital stay, complications, and readmission rates. Statistical analysis utilized ANOVA and chi-square tests, with significance set at p < 0.05. A total of 115 patients (33 control, 41 learning curve, 41 experienced) were included. No significant differences were found in demographics. Subsidence was comparable across groups (p = 0.381). AP canal filling showed no significant differences (p = 0.839), but lateral canal filling was greater in the rectangular stem groups ( p<0.001 ). Functional outcomes (p = 0.646), complications (p = 0.318), and readmission rates (p = 0.402) were similar across groups. However, hospital stay was significantly shorter in the rectangular stem groups ( p = 0.015 ). The introduction of a new rectangular femoral stem did not result in a significant learning curve affecting subsidence, complications, or functional outcomes. The stem demonstrated improved lateral canal filling and was associated with reduced hospital stay, suggesting a safe transition to this design without compromising early outcomes.
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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 | 0.002 | 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 | 0.001 |
| 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".