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            <title>Concomitant Use of Multiple Nephrotoxins Including Renal Hypoperfusion Medications Causes Vancomycin-Associated Nephrotoxicity : Combined Retrospective Analyses of Two Real-World Databases / &#x5742;&#x6771;, &#x8cb4;&#x53f8;&#x307b;&#x304b;</title>
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            <pubDate>Fri, 12 Jun 2026 12:46:47 +0900</pubDate>
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            <title>&#x96fb;&#x5b50;&#x30ab;&#x30eb;&#x30c6;&#x3092;&#x7528;&#x3044;&#x305f;&#x6cbb;&#x7642;&#x306e;&#x5b66;&#x7fd2;&#x3068;&#x63a8;&#x85a6;&#x306b;&#x95a2;&#x3059;&#x308b;&#x7814;&#x7a76; / Hoang, Khanh Hung</title>
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            <description>Hoang, Khanh Hung. &#x96fb;&#x5b50;&#x30ab;&#x30eb;&#x30c6;&#x3092;&#x7528;&#x3044;&#x305f;&#x6cbb;&#x7642;&#x306e;&#x5b66;&#x7fd2;&#x3068;&#x63a8;&#x85a6;&#x306b;&#x95a2;&#x3059;&#x308b;&#x7814;&#x7a76;. 2019-09</description>
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            <pubDate>Fri, 13 Sep 2024 18:06:07 +0900</pubDate>
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            <title>MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan &#xff0d;Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data / &#x6e21;&#x908a;, &#x6d69;</title>
            <link>https://ndlsearch.ndl.go.jp/books/R000000025-I013910005697756</link>
            <description>&#x6e21;&#x908a;, &#x6d69;. MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan &#xff0d;Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data. PLOS (Public Library of Science), 2021-09-08</description>
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            <link>https://ndlsearch.ndl.go.jp/books/R000000025-I010070006501985</link>
            <description>&#x6edd;, &#x6075;&#x6d25;; &#x8efd;&#x90e8;, &#x7531;&#x6709;&#x5b50;; &#x8c37;&#x5ddd;, &#x5343;&#x4f73;&#x5b50;; &#x9ad9;&#x5c71;, &#x8a69;&#x7a42;; &#x5185;&#x7530;, &#x7f8e;&#x4fdd;; &#x5c0f;&#x5009;, &#x90a6;&#x5b50;; &#x9ad9;&#x6728;, &#x521d;&#x5b50;. &#x57fa;&#x790e;&#x770b;&#x8b77;&#x5b66;&#x5b9f;&#x7fd2;&#x306b;&#x5411;&#x3051;&#x305f;&#x6a21;&#x64ec;&#x96fb;&#x5b50;&#x30ab;&#x30eb;&#x30c6;&#x6f14;&#x7fd2;&#x306e;&#x5b66;&#x7fd2;&#x6210;&#x679c;&#x3068;&#x8ab2;&#x984c;. &#x8056;&#x5fb3;&#x5927;&#x5b66;, 2024-03</description>
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            <title>&#x81e8;&#x5e8a;&#x7269;&#x8a9e;&#x304b;&#x3089;&#x306e;&#x6642;&#x9593;&#x60c5;&#x5831;&#x306e;&#x62bd;&#x51fa; / Moharasan, Gandhimathi</title>
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            <description>Moharasan, Gandhimathi. &#x81e8;&#x5e8a;&#x7269;&#x8a9e;&#x304b;&#x3089;&#x306e;&#x6642;&#x9593;&#x60c5;&#x5831;&#x306e;&#x62bd;&#x51fa;. 2019-03</description>
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            <title>Concomitant Use of Multiple Nephrotoxins Including Renal Hypoperfusion Medications Causes Vancomycin-Associated Nephrotoxicity : Combined Retrospective Analyses of Two Real-World Databases 77 6 / &#x5742;&#x6771;, &#x8cb4;&#x53f8;&#x307b;&#x304b;</title>
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            <pubDate>Sat, 04 Jul 2026 21:44:01 +0900</pubDate>
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            <link>https://ndlsearch.ndl.go.jp/books/R100000039-I12665212</link>
            <description>&#x9152;&#x7530;, &#x62d3;&#x4e5f;. &#x533b;&#x7642;&#x73fe;&#x5834;&#x306e;&#x5b9f;&#x8df5;&#x77e5;&#x306b;&#x57fa;&#x3065;&#x304f;&#x30a2;&#x30e9;&#x30fc;&#x30c8;&#x30d5;&#x30ec;&#x30fc;&#x30e0;&#x30ef;&#x30fc;&#x30af;&#x306e;&#x958b;&#x767a;. 2018-10-31</description>
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            <description>Hoang, Khanh Hung. &#x96fb;&#x5b50;&#x30ab;&#x30eb;&#x30c6;&#x3092;&#x7528;&#x3044;&#x305f;&#x6cbb;&#x7642;&#x306e;&#x5b66;&#x7fd2;&#x3068;&#x63a8;&#x85a6;&#x306b;&#x95a2;&#x3059;&#x308b;&#x7814;&#x7a76;. 2019-09</description>
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            <pubDate>Thu, 30 Oct 2025 00:12:06 +0900</pubDate>
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            <title>MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan &#xff0d;Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data 16 9 / &#x6e21;&#x908a;, &#x6d69;</title>
            <link>https://ndlsearch.ndl.go.jp/books/R100000039-I12694243</link>
            <description>&#x6e21;&#x908a;, &#x6d69;. MIHARI project, a preceding study of MID-NET, adverse event detection database of Ministry Health of Japan &#xff0d;Validation study of the signal detection of adverse events of drugs using export data from EMR and medical claim data 16 9. PLOS (Public Library of Science), 2021-09-08; 16 9&lt;br&gt;We studied the effectiveness of the direct data collection from electronic medical records (EMR) when it is used for monitoring adverse drug events and also detection of already known adverse events. In this study, medical claim data and SS-MIX2 standardized storage data were used to identify four diseases (diabetes, dyslipidemia, hyperthyroidism, and acute renal failure) and the validity of the outcome definitions was evaluated by calculating positive predictive values (PPV). The maximum positive predictive value (PPV) for diabetes based on medical claim data was 40.7% and that based on prescription data from SS-MIX2 Standardized Storage was 44.7%. The PPV for dyslipidemia was 50% or higher under either of the conditions. The PPV for hyperthyroidism based on disease name data alone was 20?30%, but exceeded 60% when prescription data was included in the evaluation. Acute renal failure was evaluated using information from medical records in addition to the data. The PPV for acute renal failure based on the data of disease names and laboratory examination results was slightly higher at 53.7% and increased to 80?90% when patients who previously had a high serum creatinine (Cre) level were excluded. When defining a disease, it is important to include the condition specific to the disease; furthermore, it is very useful if laboratory examination results are also included. Therefore, the inclusion of laboratory examination results in the definitions, as in the present study, was considered very useful for the analysis of multi-center SS-MIX2 standardized storage data.</description>
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            <pubDate>Wed, 29 Oct 2025 02:53:03 +0900</pubDate>
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            <link>https://ndlsearch.ndl.go.jp/books/R100000039-I11569936</link>
            <description>&#x30b5;&#x30de;&#x30eb;, &#x30a8;&#x30eb; &#x30d8;&#x30ed;&#x30fc;. &#x96fb;&#x5b50;&#x30ab;&#x30eb;&#x30c6;&#x30b7;&#x30b9;&#x30c6;&#x30e0;&#x306e;&#x9032;&#x5316;&#x7684;&#x8a2d;&#x8a08;. Kyoto University, 2019-09-24. DOI:10.14989/doctor.k22097; 10.14989/doctor.k22097</description>
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            <link>https://ndlsearch.ndl.go.jp/books/R100000039-I11583212</link>
            <description>&#x30eb;&#x30af;&#x30de;&#x30f3;, &#x30d8;&#x30ea;&#x30e4;&#x30ef;&#x30f3;. &#x533b;&#x4e8b;&#x30b3;&#x30fc;&#x30c9;&#x8a18;&#x9332;&#x8005;&#x306e;&#x8996;&#x70b9;&#x3092;&#x8e0f;&#x307e;&#x3048;&#x305f;&#x30b3;&#x30f3;&#x30d4;&#x30e5;&#x30fc;&#x30bf;&#x306b;&#x3088;&#x308b;&#x8a3a;&#x7642;&#x9332;&#x8a18;&#x8f09;&#x652f;&#x63f4;. Kyoto University, 2020-09-23. DOI:10.14989/doctor.k22804; 10.14989/doctor.k22804</description>
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            <pubDate>Sat, 07 Mar 2026 14:43:23 +0900</pubDate>
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            <description>Moharasan, Gandhimathi. &#x81e8;&#x5e8a;&#x7269;&#x8a9e;&#x304b;&#x3089;&#x306e;&#x6642;&#x9593;&#x60c5;&#x5831;&#x306e;&#x62bd;&#x51fa;. 2019-03</description>
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