An intraoperative load sensor did not improve the early postoperative results of posterior‐stabilized TKA for osteoarthritis with varus deformities
Bibliographic record
Abstract
PURPOSE: In the present study, the early results of sensor-assisted versus manually balanced posterior-stabilized total knee arthroplasty (TKA) for osteoarthritis with varus deformities were prospectively compared. METHODS: Fifty patients undergoing sensor-assisted TKA (group S) and 50 patients receiving manually balanced TKA (group M) were prospectively compared. The groups did not differ in terms of demographics, preoperative clinical status, or severity of deformity. The knee and function scores (KS and FS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), and range of motion (ROM) were evaluated clinically. The mechanical axes and positions of components were assessed radiographically. In sensor-assisted TKA, the medial and lateral compartment loads were compared based on the patellar positions of inversion and eversion. RESULTS: There was no between-group difference in the postoperative KS or FS (n.s., respectively). The average postoperative WOMAC score was 17.0 in group S and 18.0 in group M (n.s.). The ROM was 131.2° in group S and 130.8° in group M (n.s.). Neither the postoperative alignment of the mechanical axis nor the component positioning differed between the groups (n.s.). In sensor-assisted TKA, the difference between the medial and lateral compartment loads was less than 15 lbs (6.8 kg) in each knee. The lateral compartment load increased after patellar eversion (p < 0.001). CONCLUSION: There are concerns about the cost-benefit ratio of the intraoperative load sensor, despite its advantage of more precisely assessing ligament balance without patellar eversion, which resulted in a smaller lateral gap. A long-term follow-up study with a large cohort is required. LEVEL OF EVIDENCE: 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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".