Niranjan Karnik to Opioid-Related Disorders
This is a "connection" page, showing publications Niranjan Karnik has written about Opioid-Related Disorders.
Connection Strength
7.131
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Building a statewide network of MOUD expertise using tiered ECHO? mentoring opportunities. Drug Alcohol Depend. 2023 04 01; 245:109823.
Score: 0.758
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Response to Kelley & Incze: there is certainly more work to do for the OUD-COS. Addiction. 2023 01; 118(1):196.
Score: 0.741
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Development and multimodal validation of a substance misuse algorithm for referral to treatment using artificial intelligence (SMART-AI): a retrospective deep learning study. Lancet Digit Health. 2022 06; 4(6):e426-e435.
Score: 0.720
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The opioid use disorder core outcomes set (OUD-COS) for treatment research: findings from a Delphi consensus study. Addiction. 2022 09; 117(9):2438-2447.
Score: 0.715
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Expanding Access to Medications for Opioid Use Disorder Treatment Through Incentivized Continuing Education. J Contin Educ Health Prof. 2022 01 01; 42(1):e102-e105.
Score: 0.700
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External validation of an opioid misuse machine learning classifier in hospitalized adult patients. Addict Sci Clin Pract. 2021 03 17; 16(1):19.
Score: 0.662
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The substance use intervention team: A hospital-based intervention and outpatient clinic to improve care for patients with substance use disorders. Am J Health Syst Pharm. 2021 02 08; 78(4):345-353.
Score: 0.658
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Core outcomes set for research on the treatment of opioid use disorder (COS-OUD): the National Institute on Drug Abuse Clinical Trials Network protocol for an e-Delphi consensus study. Trials. 2021 Jan 28; 22(1):102.
Score: 0.656
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The Substance Use Intervention Team: A Preliminary Analysis of a Population-level Strategy to Address the Opioid Crisis at an Academic Health Center. J Addict Med. 2019 Nov/Dec; 13(6):460-463.
Score: 0.602
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Subtypes in patients with opioid misuse: A prognostic enrichment strategy using electronic health record data in hospitalized patients. PLoS One. 2019; 14(7):e0219717.
Score: 0.590
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Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups. J Am Med Inform Assoc. 2021 10 12; 28(11):2393-2403.
Score: 0.172
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Publicly available machine learning models for identifying opioid misuse from the clinical notes of hospitalized patients. BMC Med Inform Decis Mak. 2020 04 29; 20(1):79.
Score: 0.156