oh wow this @ thing is cool, thanks for showing it me
@Cressida Also, thank you for correcting me, my mistake for misunderstanding what you meant regarding meta-analysis. I read the book back when I was still working, I do not recall it discussing specifically the short comings of the two studies in question. As I said, I will reread it. In general, many studies are ultimately found wanting, including many of the wage gap studies I had come across, an example from my previous post:
Wage gap! women make ~20% less than men! ----> then I found they grouped ALL women vs ALL men, regardless of profession or qualifications.
Wage gap! women make ~15% less than men! ----> then I found they grouped ALL women vs ALL men in the same profession, not actual jobs, just professions
Wage gap! women make ~10% less than men! ----> then I found they grouped ALL women vs ALL men in the same industry, not within the same employer
Wage gap! women make ~5% less than men! ----> this turned out to be the one in question, ie, same job title, same company, same qualifications, then I noticed NO ONE even mentioned pay grades (wilful omission or ignorance?), which as we all know, can easily vary 10% within the same job title.
Personally, when I question the validity of something, I am inclined to go over every study in question and investigate the flaws in design or interpretations. I have done so in my numerous posts here, I hope that is clear for all to see. I have yet to meet an "adversary" (I use this term loosely, don't take it too seriously) that has done (or even
inclined to do) the same in this thread, many prefer to simply focus on my "
tones and manners" instead of doing some analytical/critical thinking and focus on the issue. It is completely up to you to decide if you want to look into the two studies to discover flaws yourself or you could just "believe" them to be part of the flawed studies without even looking at them. As far as I can tell there are no real academic attempts to refute these studies.
I salute all who contributed to the discussion in an analytical and rational manner, especially the ones that provided studies and possible hypothesis. Especially the Stanford link, which I will study further for more insights.
AliEli,
May I request the terms of endearments to be dropped from our exchanges. I am not one for endearments from people on the internet, thank you. I am sorry if I had somehow offended you. You had only mentioned you were in HC, without further info, the odds of someone in the field with extensive background in inflammatory studies were low; playing the odds, I made the suggestion, clearly, I was mistaken.
Your qualifications/experiences will no doubt enable you to read the whole document without assistance, I hope you find the document worth reading, and
I welcome any studies that refute my take-away, that's the reason I am here in the first place (although it seems, it went from evidence about overtly sexism induced wage gap to studies that physicians should place more weight on patients' self reported symptoms than objective tests).
From what I remember, C-reactive proteins was brought up as a marker, but the physicians (on the day) stressed using Fecal calprotectin? to be more accurate in evaluations.
Regarding the "less certain picture" from the quote, remember this is a consensus clinical practice guideline. Many of these documents have degrees of uncertainty "built-in" to provide some wiggle room (legal leeway) should some unforeseen event were to happen, not unlike some investment prospectus. Back in the day when I wrote exploration/seismic reports I employed similar tactics as well. The tone of the discussion at the seminar was quite unequivocal: objective test results are much more important than patients' self reported symptoms. ie, hard data > people's narratives.
I can't help but to notice our current discussion is more about my "
conviction" on whether data tells the true story better than narratives, instead of a discussion about the actual data and analysis itself. May I suggest we spend more time on the data presented and conclusions drawn. After all, I have admitted
my own bias is what the data says. Thank you.