A Retrospective Study to Compare Patient Outcomes from Standard Total Knee Arthroplasty (TKA) versus Navigation-Guided Arthroplasty Using the Brainlab Software-Guided Surgical System at a Center in Hebei Province January 2021 to July 2023
Bibliographic record
Abstract
BACKGROUND This retrospective study aimed to compare patient outcomes from standard total knee arthroplasty (TKA) vs navigation-guided arthroplasty using the Brainlab software-guided surgical system at Cangzhou Hospital of Integrated TCM-WM, Hebei, Hebei Province, China from January 2021 to July 2023. MATERIAL AND METHODS A total of 239 patients who underwent total knee arthroplasty in Cangzhou Hospital of Integrated TCM-WM, Hebei from January 2021 to July 2023 were retrospectively analyzed. According to the inclusion criteria, 212 eligible patients were selected for analysis and divided into a Navigation Group (NG) (n=105) and a Traditional Group (TG) (n=107) according to surgical method used. Outcomes measured included duration of disease, operative time, intraoperative blood loss volume, postoperative length of hospital stay, and pain measured by the hospital for special surgery knee score (HSS), Western Ontario and McMaster University Osteoarthritis Index (WOMAC), and forgotten joint score (FJS). RESULTS The comparison of perioperative results between the 2 groups showed that the incision length in the NG was significantly longer than that in the TG (P<0.001, 95% Cl 2.59-3.35). At 3 months after surgery, the HSS score of the NG was statistically higher than that of the TG (P=0.002, 95% Cl 3.42-4.46); the WOMAC score of the NG was lower than that of the TG (P<0.001, 95% Cl -4.41-2.87); and the FJS score of the NG was significantly higher than that of the TG (P=0.003, 95% Cl 2.39-3.67). CONCLUSIONS Compared with conventional TKA, use of the Brainlab navigation system is associated with a longer incision, more accurate implantation position of the prosthesis, faster recovery of knee joint function, and helps patients to "forget" about their knee prosthesis in the short term.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".