Variability between the trial and final implant measurements during the sensor‐guided total knee arthroplasty
Bibliographic record
Abstract
PURPOSE: Compartmental load-sensing technology has been used in the attempt to achieve optimal soft tissue balance during total knee arthroplasty (TKA). This study was conducted to investigate the validity of such use of intraoperative sensing technology. METHODS: Ninety-three knees scheduled to undergo total knee arthroplasty for knee osteoarthritis with a tibial sensor were prospectively enrolled. Measurements were divided into three groups according to the three different time points of intraoperative load testing: group Trial (with the trial components), group Final (with the definitive cemented implants and an open joint capsule), and group Closed (with the definitive cemented implants and a closed joint capsule). Load measurements and component rotational alignments were documented at 10°, 30°, 45°, 90°, and 120° of flexion for all three groups, and compared. One year postoperatively, the joint line obliquity angle was obtained radiographically in the valgus and varus stress views at 10° and 30° flexion to evaluate the clinical instability. The Knee Society, Hospital for Special Surgery, and Western Ontario McMaster Universities Osteoarthritis Index scores were used to determine functional outcomes. The correlations of the above outcomes with intraoperative load were evaluated. RESULTS: There were significant differences in medial and lateral loads at all flexion angles (except at a 120° lateral load) between group Trial and group Final (p < 0.05). Tibial trays were internally rotated to a significantly higher degree in group Final than in group Trial (p = 0.010). The lateral compartmental load significantly decreased after patellar inversion (p = 0.037). There were no correlations of intraoperative load with clinical instability and functional outcomes. CONCLUSION: Significant variability was observed between the trial and final implant measurements and intraoperative sensing data were not correlated with instability or functional outcomes over a 1-year period. Therefore, intraoperative sensor technology provides limited feedback and clinical efficacy in the adjustment of the soft tissue balance during TKA. LEVEL OF EVIDENCE: Level II.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".