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Comment
. 2022 Apr;41(4):1041-1042.
doi: 10.1002/nau.24901. Epub 2022 Feb 23.

Commentary on "Predicting outcomes after intradetrusor onabotulinumtoxinA for nonneurogenic urgency incontinence in women"

Glenn T Werneburg  1 , Sandip P Vasavada  1
Affiliations
Comment

Commentary on "Predicting outcomes after intradetrusor onabotulinumtoxinA for nonneurogenic urgency incontinence in women"

Glenn T Werneburg et al. Neurourol Urodyn. 2022 Apr.
No abstract available

Keywords: artificial intelligence; machine learning; onabotulinumtoxinA; overactive bladder; prognostication; urge urinary incontinence.

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References

REFERENCES

    1. Hendrickson WK, Xie G, Rahn DD, et al. Predicting outcomes after intradetrusor onabotulinumtoxina for non-neurogenic urgency incontinence in women. Neurourol Urodyn. 2021;41:432-447.
    1. Visco AG, Brubaker L, Richter HE, et al. Anticholinergic therapy vs. onabotulinumtoxinA for urgency urinary incontinence. N Engl J Med. 2012;367(19):1803-1813.
    1. Amundsen CL, Richter HE, Menefee SA, et al. OnabotulinumtoxinA vs sacral neuromodulation on refractory urgency urinary incontinence in women: a randomized clinical trial. JAMA. 2016;316(13):1366-1374.
    1. Emerson S, Kennedy R, O'Shea L, O'Brien J. Trends and applications of machine learning in quantitative finance. Paper presented at: 8th International Conference on Economics and Finance Research (ICEFR 2019). 2019.
    1. Rhee J, Im J. Meteorological drought forecasting for ungauged areas based on machine learning: using long-range climate forecast and remote sensing data. Agricult Forest Meterol. 2017;237:105-122.

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