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Automatic classification of published clinical articles using metadata instead of content

Automatic classification of published clinical articles using metadata instead of content. Centre for Health Informatics Australian Institute of Health Innovation Adam G. Dunn, Guy Tsafnat , Enrico Coiera.

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Automatic classification of published clinical articles using metadata instead of content

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  1. Automatic classification of published clinical articles using metadata instead of content Centre for Health Informatics Australian Institute of Health Innovation Adam G. Dunn, Guy Tsafnat, Enrico Coiera

  2. Our aim is to examine the clinical evidence for drugs to identify (plus measure & find indicators for)the biases that make drugs look safe and effective when they aren’t.

  3. Prasad’s definitions of evidence reversals: • “We use the term reversal to signify the phenomenon of a new trial—superior to predecessors because of better design, increased power, or more appropriate controls—contradicting current clinical practice… either less effective than previously thought or harmful” • 40% of articles in NEJM 2001-2010 testing the standard of care were defined as a reversal, where tested interventions were found to be worse than prior standards. • Prasad et al. Reversals of established medical practices: Evidence to abandon ship. JAMA. 2012;307(1):37-38. • Prasad et al. The frequency of medical reversal. Archives of Internal Medicine. 2011;171(18):1675-1676. • Prasad et al. A decade of reversal: an analysis of 146 contradicted medical practices. Mayo Clinic Proceedings. 2013;88(8):790-798.

  4. Neuraminidase inhibitors Muthuri 2014: “encourage early initiation of neuraminidase inhibitor treatment in outpatients who are appreciably unwell with suspected or confirmed influenza, or at increased risk of complications, including those with influenza A H3N2 or influenza B.” Beck 2013: “NAIs should be deployed during a future pandemic for either post-exposure prophylaxis or treatment depending on national policy considerations and logistics.” Jefferson 2014: “…We believe these findings provide reason to question the stockpiling of oseltamivir, its inclusion on the WHO list of essential drugs, and its use in clinical practice as an anti-influenza drug.” Wang 2012: “The benefit of oseltamivir and zanamivir in preventing the transmission of influenza in households is modest and based on weak evidence.”

  5. Neuraminidase inhibitors • 152 systematic and narrative reviews (2005-2013) • 510 authors (407 unique researchers) • 10,086 citations (4,574 unique documents)

  6. Co-authorship network k = 11 X = 73 20 31 28 accuracy = 0.6645 precision = 0.7019 recall = 0.7849 f1-score = 0.7411 ---------------------------------

  7. Citation similarity network k = 17 X = 83 10 34 25 accuracy = 0.7105 precision = 0.7094 recall = 0.8925 f1-score = 0.7905 ---------------------------------

  8. Industry affiliations

  9. Resistance& safety topics

  10. Thank you • AG Dunn, D Arachi, J Hudgins, G Tsafnat, E Coiera, FT Bourgeois (2014) Financial Conflicts of Interest and Conclusions About Neuraminidase Inhibitors for Influenza, Annals of Internal Medicine, [accepted]. • X Zhou, Y Wang, G Tsafnat, E Coiera, FT Bourgeois, AG Dunn (2014) Citations alone were enough to predict favourable conclusions in reviews of neuraminidase inhibitors. Journal of Clinical Epidemiology [accepted]. • AG Dunn, E Coiera (2014) Should comparative effectiveness research ignore industry-funded data? Journal of Comparative Effectiveness Research, [accepted]. • K Robinson, AG Dunn, G Tsafnat, P Glasziou (2014) Citation networks of related trials are often disconnected: implications for bidirectional citation searches, Journal of Clinical Epidemiology, 67 (7): 793-799. doi:10.1016/j.jclinepi.2013.11.015. • AG Dunn, FT Bourgeois, E Coiera (2013) Industry Influence in Evidence Production, Journal of Epidemiology & Community Health, 67:537-538. doi:10.1136/jech-2013-202344. • AG Dunn, B Gallego, E Coiera (2012) Industry influenced evidence production in collaborative research communities: A network analysis, Journal of Clinical Epidemiology, 65(5): 535-543. doi:10.1016/j.jclinepi.2011.10.010.

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