Outcomes and Predictors of Success for Arthroscopic Lysis of Adhesions for the Stiff Total Knee Arthroplasty
Bibliographic record
Abstract
A stiff total knee arthroplasty (TKA) can manifest as pain and decreased range of motion (ROM). When conservative management including physical therapy and manipulation under anesthesia fails, arthroscopic lysis of adhesions (LOA) is frequently pursued. The authors examined the efficacy of LOA for the stiff TKA. They retrospectively reviewed the records of 18 patients who underwent LOA for stiff TKA between April 2013 and June 2016 and who failed to meet 90° ROM at 6 weeks postoperatively. They recorded patient demographics, ROM prior to TKA, and incidence of manipulation under anesthesia between initial TKA and LOA as well as pre- and postoperative outcomes of ROM, pain, and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores. The mean time from TKA to LOA was 117 days, with a mean follow-up of 449 days. Mean improvements in ROM flexion contracture, flexion, and arc were 6.11° (P<.05), 29.45° (P<.001), and 35.56° (P<.001), respectively. Range of motion improved for 17 of 18 patients. The WOMAC was completed by 15 of 18 patients, with a mean improvement in scores of 32.23% (P<.001); all 15 patients with available WOMAC scores improved. Pain score improved by a mean of 2.17 (P<.001), with 14 of 18 patients reporting decreased pain. Improvements in flexion contracture, flexion, ROM arc, WOMAC scores, and pain were all statistically significant (P<.05). Age, weight, body mass index, and time to LOA were found to be statistically significant predictors of outcome. Finally, pre-TKA and pre-LOA ROM parameters were found to be statistically significant predictors of post-LOA ROM outcomes. [Orthopedics. 2017; 40(6):e1062-e1068.].
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".