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      Signs & Symptoms of Dextromethorphan Exposure from YouTube

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          Abstract

          Detailed data on the recreational use of drugs are difficult to obtain through traditional means, especially for substances like Dextromethorphan (DXM) which are available over-the-counter for medicinal purposes. In this study, we show that information provided by commenters on YouTube is useful for uncovering the toxicologic effects of DXM. Using methods of computational linguistics, we were able to recreate many of the clinically described signs and symptoms of DXM ingestion at various doses, using information extracted from YouTube comments. Our study shows how social networks can enhance our understanding of recreational drug effects.

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          Automated identification of postoperative complications within an electronic medical record using natural language processing.

          P Elkin, Theodore Speroff, Emery Brown … (2011)
          Currently most automated methods to identify patient safety occurrences rely on administrative data codes; however, free-text searches of electronic medical records could represent an additional surveillance approach. To evaluate a natural language processing search-approach to identify postoperative surgical complications within a comprehensive electronic medical record. Cross-sectional study involving 2974 patients undergoing inpatient surgical procedures at 6 Veterans Health Administration (VHA) medical centers from 1999 to 2006. Postoperative occurrences of acute renal failure requiring dialysis, deep vein thrombosis, pulmonary embolism, sepsis, pneumonia, or myocardial infarction identified through medical record review as part of the VA Surgical Quality Improvement Program. We determined the sensitivity and specificity of the natural language processing approach to identify these complications and compared its performance with patient safety indicators that use discharge coding information. The proportion of postoperative events for each sample was 2% (39 of 1924) for acute renal failure requiring dialysis, 0.7% (18 of 2327) for pulmonary embolism, 1% (29 of 2327) for deep vein thrombosis, 7% (61 of 866) for sepsis, 16% (222 of 1405) for pneumonia, and 2% (35 of 1822) for myocardial infarction. Natural language processing correctly identified 82% (95% confidence interval [CI], 67%-91%) of acute renal failure cases compared with 38% (95% CI, 25%-54%) for patient safety indicators. Similar results were obtained for venous thromboembolism (59%, 95% CI, 44%-72% vs 46%, 95% CI, 32%-60%), pneumonia (64%, 95% CI, 58%-70% vs 5%, 95% CI, 3%-9%), sepsis (89%, 95% CI, 78%-94% vs 34%, 95% CI, 24%-47%), and postoperative myocardial infarction (91%, 95% CI, 78%-97%) vs 89%, 95% CI, 74%-96%). Both natural language processing and patient safety indicators were highly specific for these diagnoses. Among patients undergoing inpatient surgical procedures at VA medical centers, natural language processing analysis of electronic medical records to identify postoperative complications had higher sensitivity and lower specificity compared with patient safety indicators based on discharge coding.
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            2011 Annual report of the American Association of Poison Control Centers' National Poison Data System (NPDS): 29th Annual Report.

            Philip Bronstein, B Rumack, Louis Cantilena … (2012)
            This is the 29th Annual Report of the American Association of Poison Control Centers' (AAPCC) National Poison Data System (NPDS). As of 1 July 2011, 57 of the nation's poison centers (PCs) uploaded case data automatically to NPDS. The upload interval was 8.43 [6.29, 13.7] (median [25%, 75%]) minutes, creating a near real-time national exposure and information database and surveillance system. We analyzed the case data tabulating specific indices from NPDS. The methodology was similar to that of previous years. Where changes were introduced, the differences are identified. Poison center cases with medical outcomes of death were evaluated by a team of 38 medical and clinical toxicologist reviewers using an ordinal scale of 1-6 to assess the Relative Contribution to Fatality (RCF) of the exposure to the death. In 2011, 3,624,063 closed encounters were logged by NPDS: 2,334,004 human exposures, 80,266 animal exposures, 1,203,282 information calls, 6,243 human confirmed nonexposures, and 268 animal confirmed nonexposures. Total encounters showed an 8.3% decline from 2010, while health care facility exposure calls increased by 4.8%. Human exposures with less serious outcomes decreased by 3.4% while those with more serious outcomes (moderate, major or death) increased by 6.8%. All information calls decreased by 17.9% and health care facility (HCF) information calls decreased by 2.9%, Medication identification requests (Drug ID) decreased by 24.1%, and human exposures reported to US poison centers decreased by 2.2%. The top 5 substance classes most frequently involved in all human exposures were analgesics (11.7%), cosmetics/personal care products (8.0%), household cleaning substances (7.0%), sedatives/hypnotics/antipsychotics (6.1%), and foreign bodies/toys/miscellaneous (4.1%). Analgesic exposures as a class increased most rapidly (10,134 calls/year) over the last 11 years. The top 5 most common exposures in children aged 5 years or less were cosmetics/personal care products (14.0%), analgesics (9.9%), household cleaning substances (9.2%), foreign bodies/toys/miscellaneous (6.9%), and topical preparations (6.6%). Drug identification requests comprised 59.5% of all information calls. NPDS documented 2,765 human exposures resulting in death with 1,995 human fatalities judged related (RCF of 1-Undoubtedly responsible, 2-Probably responsible, or 3-Contributory). These data support the continued value of poison center expertise and need for specialized medical toxicology information to manage the more severe exposures, despite a decrease in calls involving less severe exposures. Unintentional and intentional exposures continue to be a significant cause of morbidity and mortality in the US. The near real-time, always current status of NPDS represents a national public health resource to collect and monitor US exposure cases and information calls. The continuing mission of NPDS is to provide a nationwide infrastructure for public health surveillance for all types of exposures, public health event identification, resilience response and situational awareness tracking. NPDS is a model system for the nation and global public health.
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              Amphetamine primes motivation to gamble and gambling-related semantic networks in problem gamblers.

              Martin Zack, Rebecca Poulos (2003)
              Previous research suggests that gambling can induce effects that closely resemble a psychostimulant drug effect. Modest doses of addictive drugs can prime motivation for drugs with similar properties. Together, these findings imply that a dose of a psychostimulant drug could prime motivation to gamble in problem gamblers. This study assessed priming effects of oral D-amphetamine (AMPH) (30 mg) in a within-subject, counter-balanced, placebo-controlled design in problem gamblers (n=10), comorbid gamblerdrinkers (n=6), problem drinkers (n=8), and healthy controls (n=12). Modified visual analog scales assessed addictive motivation and subjective effects. A modified rapid reading task assessed pharmacological activation of words from motivationally relevant and irrelevant semantic domains (Gambling, Alcohol, Positive Affect, Negative Affect, Neutral). AMPH increased self-reported motivation for gambling in problem gamblers. Severity of problem gambling predicted positive subjective effects of AMPH and motivation to gamble under the drug. There was little evidence that AMPH directly primed motivation for alcohol in problem drinkers. On the reading task, AMPH produced undifferentiated improvement in reading speed to all word classes in Nongamblers. By contrast, in the two problem gambler groups, AMPH improved reading speed to Gambling words while profoundly slowing reading speed to motivationally irrelevant Neutral words. The latter finding was interpreted as directly congruent with models, which contend that priming of addictive motivation involves a linked suppression of motivationally irrelevant stimuli. This study provides experimental evidence that psychostimulant-like neurochemical activation is an important component of gambling addiction.
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                Author and article information

                Contributors
                Renaud Lambiotte: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2014
                Publication date (Electronic): 12 February 2014
                Volume: 9
                Issue: 2
                Electronic Location Identifier: e82452
                Affiliations
                [1 ]Ichan School of Medicine at Mount Sinai, New York, New York, United States of America
                [2 ]Rutgers New Jersey Medical School, Newark, New Jersey, United States of America
                [3 ]Division of Medical Toxicology, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
                [4 ]Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, United States of America
                University of Namur, Belgium
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: MC AM NG AFM. Performed the experiments: MC EP JS AM. Analyzed the data: MC EP NG. Wrote the paper: MC EP NG.

                Article
                Publisher ID: PONE-D-13-21958
                DOI: 10.1371/journal.pone.0082452
                PMC ID: 3922701
                PubMed ID: 24533044
                SO-VID: 2cd5193c-01bb-4522-8f79-283bc56c6b72
                Copyright statement: Copyright @ 2014
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 28 May 2013
                Date accepted : 23 October 2013
                Page count
                Pages: 10
                Funding
                The authors have no support or funding to report.
                Categories
                Subject: Research Article
                Subject: Biology
                Subject: Computational Biology
                Subject: Natural Language Processing
                Subject: Computer Science
                Subject: Natural Language Processing
                Subject: Medicine
                Subject: Drugs and Devices
                Subject: Behavioral Pharmacology
                Subject: Recreational Drug Use
                Subject: Drug Information
                Subject: Mental Health
                Subject: Psychiatry
                Subject: Substance Abuse
                Subject: Toxicology
                Subject: Social and Behavioral Sciences
                Subject: Linguistics
                Subject: Computational Linguistics
                Subject: Sociology
                Subject: Social Networks

                ScienceOpen disciplines: Uncategorized
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                ScienceOpen disciplines: Uncategorized

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