Clinical and radiologic study of total knee replacement arthroplasty using Doujet GF bone cement (liquid-powder bone cement containing gentamicin)
Bibliographic record
Abstract
Gentamicin-loaded bone cement used in total joint arthroplasty is indispensable, as it provides stability by directly binding the surfaces of implants and bones. Depending on multiple factors, including the material of the bone cement used, common complications, such as aseptic loosening, osteolysis, and infection can occur postoperatively. In clinical practice, Doujet bone cement is easy to handle (pre-packed all-in-one system), and has shown low failure rates and non-inferior results compared with similar available products. We conducted a retrospective comparative study to analyze the clinical and radiological results of each bone cement group to establish the safety and usefulness of Doujet bone cement. From July 2020 to July 2022, we enrolled 198 patients in this study after an average follow-up period of 37 months (range, 6–48 months). In 99 patents, Doujet® bone cement (Injecta, Gunpo, Korea) was used for total knee arthroplasty (TKA), while Refobacin® bone cement (Biomet, Warsaw, IN, USA) was used in 99 patients. The average range of motion (ROM) of the knee increased by 2.4° (from 127.0° preoperatively to 129.4° postoperatively) in the Doujet group, and by 0.1° (from 128.7° to 128.8°) in the Refobacin group (P=0.701). The Western Ontario and McMaster Universities (WOMAC) osteoarthritis index scores decreased from 44.1 to 7.8 in the Doujet group, and from 44.2 to 6.3 in the Refobacin group (P=0.162). Complications, such as osteolysis or post-operative wound infection, did not occur in more than two cases in both groups. The WOMAC and ROM of the knee in both groups had no clinical differences. Both Doujet and Refobacin similarly showed low complication rates after TKA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".