In this specific case, with the extra details about the data, some questions may still be raised about the model.
The response variable,
Score, is cited as varying from 0 to 100, but was analysed using
ln(Score). But the logarithm of 0 is indeterminate, so precisely what did the authors do with value(s) equal to 0?
Similarly, what was done about the zero(s) in
Last when using
Further, it is a fair guess that the range 0 to 100 for
Score is a limitation of principle, but the model ignores that. That may or may not be a problem: a particularly strong clue is that the mean and median are both above 90, so values in the 90s may be common, and in any case values at the bounds evidently occur. This boundedness is likely to imply some nonlinearity and it is not obvious that logarithms are the way to cope with that.
The use of natural logarithmic transforms probably reflects the idea that a multiplicative model makes more sense than an additive one; it is unlikely to be driven by the skewness of the response, which from the results here is likely to be negative hence not positive. Putting these two together, it is possible that the ln transformation induces moderate outliers!
Although without the data you cannot check the calculations, I'd suggest that the quality of the analysis might fairly be guessed by the extent to which the authors discuss
The characteristics of the data. I would particularly expect to see a frank discussion of what might be awkward or problematic for any analysis and how it was handled.
Theoretical explanations and/or what kinds of models make scientific or substantive sense. For many projects, this should come first, naturally.
Their particular choices in analysis. Data very rarely can or should be modelled in just one way, so why particular models were chosen for report is vital.
More personally, I would hope to see, as diagnostic of careful work, graphs as well as tables to give a clear picture of data and/or results. Habits and traditions vary, and in some fields graphs are avoided on various grounds, say supposed lack of space, lack of an obvious graph to show off a complicated model, academic snobbery, a principle that graphs should not repeat tables, or prejudice that graphical inspection is a form of data snooping. But it is rare that authors don't have scope for, minimally, alluding to graphical exploration of their data. The lack of graphs or of evidence of graphical work often hints at superficial, naive or mechanistic work.
In short, be particularly sceptical if models just are presented without a background story or critical discussion.
A very personal, and very minute, detail is that authors who allow "Ln" to appear in print rather than "ln" lack mathematical taste, even when it starts a sentence, but it is possible that this should be blamed on reviewers, editors, or publishers' staff.