You are outraging silly person. Plagiarism has nothing to do with the study data! This is a fake argument to discredit a study. There are > 100'000 corona papers now and the introduction of most is identical. So we have 100'000 plagiarism....
The other issue I explained already twice . Treat your Alzheimer first.
So: my job here is to highlight the more outrageous Wyttenfacts - if I did every one I would get very bored.
Plagiarism shows that the author is unprofessional and likely not able to write a research paper themselves - which makes it more likely that they don't know how to conduct a trial well. It is a red flag, but does not prove anything.
It is not true that many people cut and paste text in a research paper without reference, because they know full well that plagiarism is bad for their career.
But really, plagiarism is the least of this study's problems (as are cut-and-paste raw data). There are many problems with the data as below which show it cannot be real. The author won't reply to them, but perhaps you could try and work out innocent explanations for the three I have highlighted below?
After its initial publication, the Elgazzar preprint went through two further revisions. The purported authors published their third draft on the 28th of December 2020. It featured minor corrections and further statistical information but notably also included a link to what the authors stated was a copy of the original data, which Grftr News later obtained. (The paper’s withdrawal comes in the form of a ‘fourth’ revision).
When opening what the authors claim is their original data the first thing that any reader notices is that it’s remarkably complete. In many columns data for all patients are fully listed. The second thing the reader will likely notice is that the original data do not match the author’s public results. In three of the four study arms measuring patient death as an outcome, the numbers between the paper and original data differ.
In their paper, the authors claim that four out of 100 patients died in their standard treatment group for mild and moderate COVID-19. According to the original data they uploaded, the number was 0 (the same as the ivermectin treatment group). In their ivermectin treatment group for severe COVID-19, the authors claim two patients died – the number in the uploaded raw data is four. Grftr News put these findings to the authors however has not received any reply.
The original data provided by the authors suggest that efforts to randomise patients between different groups either failed or was not attempted – despite claims to the contrary by the authors. Every patient in the severe COVID-19 group receiving standard care was an ICU patient, while the patients with severe disease in the ivermectin group were mixed between wards and ICU. The experts Grftr News spoke to confirmed this is extremely unlikely to happen by chance.
Following these initial detections, Grftr News provided a copy of the data to science fraud expert Nick Brown and asked him to analyse it. Mere hours later, Brown had already conducted an extensive preliminary analysis and agreed to take a more in-depth look. Brown’s complete findings run for several pages and have been posted to his blog.
Wherever he looked, Brown found problems, from numbers containing non-numeric characters, confusion about date formats, and—most damningly of all—multiple incidences of data being copied between patients. In column after column data is duplicated exactly – “including the typo ‘coguh’ for ‘cough’ for multiple patients”. As Brown concludes in his blog post, “the chances of any one of these duplications occurring by chance, let alone all of them, are astronomical.”
It is also unlikely that these repetitions of data are due to an innocent copy and paste error when rearranging records in the file, as some columns contain identical patterns of data with minor adjustments to one or two numbers (possibly to make the fabrication less obvious).
Brown also discovered that many other numbers between the original data and the paper do not match. In some cases, numbers were off by only a couple per cent, while in others, the numbers were off by over 10%.
Grftr News also reached out to Kyle Sheldrick, a Sydney-based doctor and researcher who had been independently looking at the paper. Sheldrick explained that even when just looking at the results in the paper, problems emerged. For example, numbers that the authors provide for several standard deviations mentioned in tables in the paper are mathematically impossible given the range of numbers provided in the same table (standard deviations are a measure of variation in a group of data points)
Many of the patients who died appear to be duplicates. For example, according to the original data, there were ‘four’ patients with the initials NME, NEM, and NES (twice), who were all males aged 51 years old, all suffered from diarrhoea, had the same blood haemoglobin levels, were all diagnosed on the 22nd of May, and all died on the 29th of May 2020. They also all share identical values in at least four other data columns.
At least a further ten deceased patients also display evidence of being duplicated. As such, duplicates make up around half of the recorded deaths. Although much of the patient data is identical, minor changes exist, further proving that a simple copy and paste error cannot be the cause of the duplicates.
Sheldrick also argued that the completeness of data is further evidence of fabrication, noting that this is incredibly unlikely to happen in real-world conditions. In a Skype call with Grftr News, Sheldrick pointed out further statistical impossibilities, for example, noting that in the data column showing results for patient blood ferritin levels, “you’ve got at least 250 results there, and of those 250, only two or three of them end in three. And that’s just not possible. The probability of that is 2 in 10 billion.” (Nick Brown’s blog post explores this issue in more detail and finds that the trailing digits of many of the numerical values in the paper are even more implausible than that.)
If the dataset the authors provided is the one used for the paper then “this is obviously fraud”, Sheldrick explained, “there is no explanation for this other than fraud.” When pushed about whether alternative explanations are possible, Sheldrick replied, “I usually bend over backwards and stretch my incredulity; it’s the equivalent of ‘maybe the knife slipped multiple times’… but there is no [good] explanation for this. There’s no machine in the world that will have that sort of terminal digit bias.”
Brown, too, is convinced that the evidence points in the direction of fabrication. “It seems impossible to me that this data file, with its obvious cloning of substantial numbers of patient records and numerous other inconsistencies, contains a true record of the study of 600 patients that Elgazzar et al. claim to have conducted.”
After Grftr News made him aware of our findings Meyerowitz-Katz also wrote a further Medium article covering this situation and its consequences.
Grftr News put these findings to the authors of the paper but has received no response.
