The Paroxetine 352 Bipolar Study Revisited: Deconstruction of Corporate and Academic Misconduct

Medical ghostwriting is the practice in which pharmaceutical companies engage an outside writer to draft a manuscript submitted for publication in the names of “honorary authors,” typically academic key opinion leaders. Using newly-posted documents from paroxetine litigation, we show how the use of ghostwriters and key opinion leaders contributed to the publication of a medical journal article containing manipulated outcome data to favor the proprietary medication. The article was ghostwritten and managed by SmithKline Beecham, now GlaxoSmithKline (GSK) and Scientific Therapeutics Information, Inc. without acknowledging their contribution in the published article. The named authors with financial ties to GSK, had little or no direct involvement in the paroxetine 352 bipolar trial results and most had not reviewed any of the manuscript drafts. The manuscript was originally rejected by peer review; however, its ultimate acceptance to the American Journal of Psychiatry was facilitated by the journal editor who also had financial ties to GSK. Thus, GSK was able to take an under-powered and non-informative trial with negative results and present it as a positive marketing vehicle for off-label promotion of paroxetine for bipolar depression. In addition to the commercial spin of paroxetine efficacy, important protocol-designated safety data were unreported that may have shown paroxetine to produce potentially harmful adverse events

Global Modelling of Food and Agriculture

The Centre for World Food Studies is developing price endogenous models focusing on agriculture and incorporating detailed agronomic information. Moreover, the structure of these models is such that they can be linked in order to represent international trade, resulting in a global equilibrium. The developing country models emphasize and analyze the problem of small and landless farmers, the role of possible ecological constraints, and the changes in the distribution of income between agriculture and non-agriculture as well as within agriculture. Model alternatives can be analyzed through changes in various parameters, e.g. direct and indirect tax rates, tariffs, buffer stocks, import and export quotas. A brief outline of the Thailand model is presented

Inter-professional education and primary care : EFPC position paper

Inter-professional education (IPE) can support professionals in developing their ability to work collaboratively. This position paper from the European Forum for Primary Care considers the design and implementation of IPE within primary care. This paper is based on workshops and is an evidence review of good practice. Enablers of IPE programmes are involving patients in the design and delivery, providing a holistic focus, focussing on practical actions, deploying multi-modal learning formats and activities, including more than two professions, evaluating formative and summative aspects, and encouraging team-based working. Guidance for the successful implementation of IPE is set out with examples from qualifying and continuing professional development programmes

Don't Loop, Iterate

I describe an iteration macro for Common Lisp that is clear, efficient, extensible, and in excellent taste.MIT Artificial Intelligence Laborator

Analyzing popular music using Spotify’s Machine Learning Audio Features

The modern ability to stream music using services such as Spotify, Pandora, and Apple music has revolutionized how it is consumed. There is now more music accessible at our fingertips than ever before in history. As someone who has played an instrument their whole life and with some music theory knowledge, I always strive to better understand what I’m listening to. This motivated me to dive into finding out how music is quantified, specifically on Spotify, and what makes up people’s musical taste. I will be approaching music tastes from 3 angles: Spotify, music critics, and my own analysis and knowledge. To do so I am using a dataset that includes the top 100 most popular songs from 2018, off the playlist made by Spotify. This represents what Spotify and the public consider to be the best of the year. I will be comparing this to two separate top 10 lists of the ’10 worst songs of 2018’ to get the critics perspective. While some of the songs were shared between both lists, of the 16 I use, interestingly 6 of them are also in the top 100 playlist. In order to attempt to understand this anomaly, I will be comparing songs based off the variables Spotify uses to quantify their music (I get into the specifics below) and analyzing the clustering and outliers I see when graphing them. I also hope to bring some of my music theory knowledge, as well as a more contextual approach into understanding why popular music can be simultaneously very successful, while also being considered poor quality