Opportunities in Total Knee Arthroplasty: Worldwide Surgeons' Perspective.
Bibliographic record
Abstract
INTRODUCTION: This study surveyed a group of US and international orthopaedic surgeons to prioritize areas of improvement in primary total knee arthroplasty (TKA). Specifically, we assessed surgeon responses regarding the top five areas of TKA needing improvement; which were stratified by: a) US surgeons, b) international surgeons, c) US surgeons' implant-brand-loyalty, and d) surgeons' years of experience and case volume. MATERIALS AND METHODS: Four hundred and eighteen surgeons who were board-certified, in practice for at least two years, spent 60% of their time in clinical practice, and performed a minimum of 25 lower extremity joint arthroplasties per year were surveyed. They chose the top five areas (among 17) needing improvement for TKA. Results were stratified by surgeons' location (US and international), implant-brand-loyalty, years of experience, and case volume. RESULTS: Functional outcomes was the top identified area for improvement (US 63% and international 71%), followed by brand loyalty (Company I 68%, other brand 59%, and multi-brand/no loyalty 66%), years of experience (early-career 64%, mid-career 63%, and late-career 75%) and case volume (low-volume 69%, mid-volume 60%, and high-volume 71%). Following this was costs for US surgeons (47%) and implant survivorship for international surgeons (57%). While costs were the next highest area for specific Company-loyal surgeons (57%), implant survivorship was the next highest area for the other two cohorts. Implant survivorship was the second most important area of improvement regardless of years of experience and for low- and mid-volume surgeons. CONCLUSION: Surgeons identified functional outcomes as the most important area needing improvement. Cost of implants was more important for American as compared to international surgeons.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".