Clinical Results of the Use of Low-Cost TKA Prosthesis in Low Budget Countries—A Narrative Review
Bibliographic record
Abstract
Despite the orthopedics markets in the US and the EU reaching a plateau, the market size in countries such as Brazil, Russia, India, and China is steadily growing. As a result, major orthopedic companies are shifting their focus towards these markets and developing products tailored to their needs. However, a significant challenge associated with this new opportunity is the requirement for the development of more affordable prostheses compared to those sold in the US and Europe. With the introduction of these lower-cost models into the market, this article aims to assess their performance in comparison to traditional models. A literature review was conducted, analyzing four parameters—the Hospital for Special Surgery Score, Knee Society Score, Range of Motion, and Western Ontario and McMaster Universities Arthritis Index—to evaluate different models. The findings indicated that low-cost models perform either equally well or, in some cases, slightly worse than traditional ones. It is worth to mention that the existing literature on this topic is limited, resulting in a relatively small number of models and studies included in this specific study. Nevertheless, this latter serves as a valuable foundation for future in-depth analyses and investigations.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".