A comparison of stiffness of six knee braces with application for posterolateral corner reconstructions
Bibliographic record
Abstract
Posterolateral corner knee injuries are clinically significant, and often require surgical reconstruction. The optimal knee brace following posterolateral corner reconstructions has not yet been determined via clinical nor biomechanical study. We sought to evaluate the stiffness of six types of knee braces to determine the ideal brace type for reducing varus forces, which may have clinical utility for posterolateral corner knee reconstruction rehabilitation. Six different groups of knee braces underwent mechanical testing: cruciate braces, cruciate braces with a valgus bend, medial unloaders, articulating sleeves, hinged braces, and tri-panel immobilizers. Each brace was fitted to the same fiberglass leg model and was secured to the testing apparatus. Force was applied under four-point bending to generate a varus moment about the artificial knee. The stiffness in Newtons per millimeter (N/mm) of each brace was calculated from the slope of the force-displacement curve. The cruciate brace with a valgus bend had the highest average stiffness at 192.61 N/mm (SD 28.53). The articulating sleeve was the least stiff with an average stiffness of 49.86 N/mm (SD 8.99). Stiffness of the cruciate brace was not statistically different compared to cruciate valgus ( p = 0.083) or medial unloader ( p = 0.098). In this experimental design, a cruciate brace with a valgus bend was shown to have the highest overall stiffness, while an articulating sleeve had the lowest stiffness. Future work will investigate whether this translates into clinical performance.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".