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John I. Shin

Researcher at Icahn School of Medicine at Mount Sinai

Publications -  23
Citations -  757

John I. Shin is an academic researcher from Icahn School of Medicine at Mount Sinai. The author has contributed to research in topics: Spinal fusion & Retrospective cohort study. The author has an hindex of 12, co-authored 20 publications receiving 554 citations.

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Frailty Index Is a Significant Predictor of Complications and Mortality After Surgery for Adult Spinal Deformity.

TL;DR: Preoperative assessment of the mFI in this patient population can be utilized to improve current risk models and was an independent predictor of postoperative complications, mortality, and reoperation in patients undergoing surgery for ASD.
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Proximal junctional kyphosis following adult spinal deformity surgery

TL;DR: Prevalence of PJK following long spinal fusion for adult spinal deformity was high but not clinically significant, and careful and detailed preoperative planning and surgical execution may reduce PJK in adult spine deformity patients.
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Simplified Frailty Index as a Predictor of Adverse Outcomes in Total Hip and Knee Arthroplasty

TL;DR: MFI ≥0.45 is an independent predictor of Clavien-Dindo grade IV complications in TKA/THA patients with greater odds ratios than age >75, body mass index ≥40, American Society of Anesthesiologists class ≥4, and nonclean wound status and should be considered for risk stratifying joint arthroplasty patients preoperatively and perhaps determining immediate postoperative destination.
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Frailty Index as a Predictor of Adverse Postoperative Outcomes in Patients Undergoing Cervical Spinal Fusion

TL;DR: The modified frailty index was shown to be an independent predictor of Clavien-Dindo grade IV complications in patients undergoing ACDF or PCF and could be used as a platform upon which more efficient risk stratification could be done with addition of other variables.
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Impact of Gender on 30-Day Complications After Primary Total Joint Arthroplasty

TL;DR: There are discrepancies in the THA or TKA complications based on gender, and the multivariate analyses confirmed gender as an independent risk factor for certain complications.