Knee Immobilization in the Immediate Post-Operative Period Following ACL Reconstruction
Bibliographic record
Abstract
OBJECTIVE: This study was designed to determine the practice patterns and rationale for knee immobilizer use in immediate post-operative period following an anterior cruciate ligament (ACL) reconstruction. DESIGN: Descriptive cross-sectional survey. SETTING: Canada. PARTICIPANTS: A random sample of 50% of Canadian orthopedic surgeons registered with the Canadian Orthopaedic Association (COA). MAIN OUTCOME MEASURE: Self-reported survey responses regarding knee immobilizer use, surgeon characteristics, graft type and type of practice. RESULTS: Complete survey response rate was 36.1% (122/338). There was a lack of consensus regarding knee immobilizer use; 47.7% of responding surgeons use a knee immobilizer in the immediate post-operative period while 52.3% do not. There were no trends in characteristics such as fellowship training, number of years in practice and type of practice between surgeons who use and do not use a knee immobilizer. The reported reasons for immobilization were: pain reduction in the post-operative period (51.6%), graft site protection (38.7%), maintaining full extension (19.4%) and habit (12.9%). The length of time the immobilizer was used ranged from 5-42 days. CONCLUSIONS: The lack of consensus reported in the current study and published literature reflects a lack of scientific evidence in the area of post-operative knee immobilization. The need for a randomized clinical trial to assess the efficacy of knee immobilizer use after ACL reconstruction is evident. The authors recommend using peri-operative pain as an outcome measure in future studies investigating immobilization in the immediate post-operative period.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".