Biomechanical Analysis of Chlorhexidine Power Irrigation to Disinfect Contaminated Anterior Cruciate Ligament Grafts
Bibliographic record
Abstract
BACKGROUND: Accidental graft contamination is a likely complication to occur in an orthopaedic sports medicine surgeon's career. There are no clinical outcome studies to direct management, and a recent survey showed that preferred management varied. Three liters of 2% chlorhexidine power irrigation has been shown to be an efficient and effective disinfection protocol; however, the biomechanical sequelae of this disinfection protocol to the graft are unknown. PURPOSE: The purpose of this study was to determine if 3 L of 2% chlorhexidine power irrigation used to disinfect contaminated anterior cruciate ligament (ACL) grafts significantly weakens the graft at time zero. STUDY DESIGN: Controlled laboratory study. METHODS: Eight fresh bovine superficial digital flexor tendons underwent disinfection protocol with 3 L of 2% chlorhexidine power irrigation. Contralateral tendons served as the control. Tendons were preconditioned and loaded to failure. RESULTS: The average ultimate failure load for the control tendons and disinfected tendons was 4081 ± 654.4 N and 4146 ± 723.2 N, respectively. The average ultimate failure stress for the control tendons and disinfected tendons was 97.10 ± 12.03 MPa and 95.18 ± 11.79 MPa, respectively. The average stiffness for the control tendons and disinfected tendons was 546.20 ± 28.16 N/mm and 539.2 ± 25.93 N/mm, respectively. The P values for ultimate failure load, ultimate failure stress, and stiffness showed no statistically significant difference between the chlorhexidine and control tendons. CONCLUSION: Disinfecting tendons with 3 L of 2% chlorhexidine power irrigation does not adversely weaken the tendon's tensile mechanical properties. CLINICAL RELEVANCE: Three liters of 2% chlorhexidine power irrigation can effectively disinfect a contaminated ACL graft without weakening the graft.
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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 | 0.001 |
| 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".