Current State of Knee Arthroplasty in Russia: Analysis of 36,350 Сases from the Register of the Vreden National Medical Research Center of Traumatology and Orthopedics
Bibliographic record
Abstract
Background.Nowadays the knee arthroplasty register of the Vreden National Medical Research Center of Traumatology and Orthopedics (hereinafter referred to as the Vreden Center) contains clinical and statistical data on more than 39,000 primary and revision knee replacements, that mimics current state of this kind of surgery in Russia. Aimofthestudy to analyze the last decade trends in primary knee arthroplasty in largest Russian arthroplasty center. Methods.Data were obtained from the register of the Vreden Center for the period from 2011 to 2022. Information on knee arthroplasty included epidemiologic and numerous peri-operative data including type of surgery and implant, degree of constrain, primary patella resurfacing etc. Resultsanddiscussion.From 2011 to 2022, 36,350 (92.3%) primary arthroplasties performed at the Vreden Center. The number of interventions increased more than twice: from 1,678 in 2011 to 3,924 in 2022. Similar trends observed in Australia and Sweden, where the number of knee arthroplasties increased by 8.2% and 8% in 2021 compared to 2020, respectively. The frequency of primary patellar resurfacing at the Vreden Center was 2.2% over the entire period of observation. On the contrary, the rate of patella replacement increased from 41% in 2005 to 76.1% in 2021 in Australia and from 24.4% in 2015 to 31.9% in 2020 in Switzerland. The partial knee arthroplasty showed enormous growth more than 14 times: from 0.3% in 2011 to 4.3% in 2022 at the Vreden Center. Worldwide unicompartmental knee replacement is still less popular than total and its number widely varies: 4.2% in the USA, 6.9% in Australia, 9.2% in Canada, 11.9% in Norway, 12.8% in Sweden, and 18.4% in Switzerland. Posterior cruciate ligament (PCL) retaining total knee arthroplasties (TKA) prevailed at the Vreden Center: 68.3%, while in other countries it utilize even more widely: 70.5% in Norway, 75% in New Zealand and 93.5% in Sweden. The total length of hospital stay (LOS) decreased dramatically from 19.6 in 2011 to 8.6 in 2022 at the Vreden Center. Nevertheless, there are still opportunities to improve it: by the way in Canada the average LOS for TKA is 2.3 and the USA 0.8 and 1.7 for partial and total arthroplasty, respectively. Conclusion.The main current trends of knee arthroplasty in Russia are the following: increase the number of surgeries, reduced LOS, TKA without patella resurfacing and with PCL retention, finally the growth of partial knee arthroplasties.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.003 |
| 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".