No difference in clinical outcomes between portable navigation and conventional instrumentation in total knee arthroplasty: A randomised trial
Bibliographic record
Abstract
Abstract Background Portable accelerometer‐based navigation devices (PAD) in total knee arthroplasty (TKA) have been proposed to combine the alignment precision of computer navigation with the efficiency of conventional instrumentation (CON). The aim of this study was to determine if PAD was more effective than CON in TKA in improving clinical outcomes at medium term follow‐up. Methods Participants undergoing primary TKA were randomly assigned to either PAD or CON. The primary outcome was the mean between‐group difference in the four subscales of the Knee injury and Osteoarthritis Outcome Score (∆KOOS4) between preoperative status and latest follow‐up. Secondary outcomes included analysis of between‐group differences in all KOOS subscales, Western Ontario and McMaster Universities Osteoarthritis Index (∆WOMAC) scores, complications and reoperation rates. Results Of the 178 participants allocated to a treatment arm, 159 (89.3%) completed follow‐up at a mean of 4.3 years (range 3.2–5.8 years). There was no statistically significant or clinically meaningful difference in ∆KOOS4 between preoperative status and latest follow‐up (PAD = 41, CON = 43; p = 0.5). There was no difference in mean ∆WOMAC scores (PAD = 39, CON = 41; p = 0.9) or ∆KOOS subscales between groups. In addition, there were no differences in complications or reoperations between groups. Conclusions PAD was not superior to CON in improving patient‐reported outcomes or reducing complications and reoperation rates at medium term follow‐up. The use of PAD in TKA to improve clinical outcomes alone cannot be justified based on the results of this study.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".