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Multiple regression analysis predictor models in exotropia surgery

Y J Gordon, E Bachar

    American Journal of Ophthalmology
    |November 1, 1980
    PubMed
    Summary
    This summary is machine-generated.

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    A statistical model using multiple regression analysis was developed for intermittent exotropia surgery. Predictor variables were identified, but further clinical trials are needed to confirm its value in predicting surgical success.

    Area of Science:

    • Ophthalmology
    • Statistical Modeling

    Background:

    • Intermittent exotropia is a common strabismus condition.
    • Surgical outcomes in strabismus can be influenced by various patient and surgical factors.

    Purpose of the Study:

    • To develop and evaluate a statistical model for predicting surgical outcomes in intermittent exotropia.
    • To identify key predictor variables influencing the effect of surgery per millimeter.

    Main Methods:

    • Multiple regression analysis was applied to data from 30 patients undergoing bilateral lateral recti recession.
    • Predictor variables associated with surgical outcome were identified.

    Main Results:

    • The model identified predictor variables accounting for significant changes in deviation per millimeter of surgery.

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  • Limitations include potential variations in predictor variables, multicollinearity, and measurement unreliability.

    • Conclusions:

    • Multiple regression analysis shows potential but requires validation through controlled clinical trials for exotropia surgery.
    • Discriminant analysis is proposed as a potential alternative statistical technique.