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At 4 months, if four out of five predictor variables were present (LR+ = 43.9, 95% CI 2.73 to 999+), the probability of non-use increased from 12 to 86% (p < 0.001).
At 8 months, if all three predictor variables were present (LR+ = 33.9, 95% CI 2.1 to 999+), the probability of non-use increased from 15 to 86% (p < 0.001).
At 12 months, if two out of three predictor variables were present (LR+ = 2.8, 95% CI 0.9 to 6.6), the probability of non-use increased from 17 to 36% (p < 0.031).
An analysis of variance was performed on patient demographic data (presence of diabetes mellitus, smoking, anticonvulsant use, steroid use, NSAID use, and method of injury) to assure no confounding variables were present in the patient populations.
Where multiple potential confounding variables were present, we used multivariate logistic regression models to determine independent predictors of important outcomes (e.g., delayed presentation).
The disease count is reported on the 729 participants in whom all required variables were present.
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If not normally distributed, continuous variables were presented as median (quartile) and compared by nonparametric test.
Categorical variables were presented as counts.
Categorical variables were presented as frequencies and percentages.
Dichotomous variables were presented as occurrence and percentage [n ].
Variables were presented as percentages.
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