3 ms·

Forget "medical professional". Forget my criteria. Just cite someone, anyone, whom YOU believe is well-respected in medicine and whose view on this agrees with

Forget "medical professional". Forget my criteria. Just cite someone, anyone, whom YOU believe is well-respected in medicine and whose view on this agrees with you. If you can't find anyone well-respected, then cite the best source you can. My point is, just cite someone. For someone who cares about science it's sure ironic that you're expecting us to trust your judgment with zero backing.

kem 10y ago
I offer this in the interests of maintaining what I see as a useful, if difficult, discussion, not to be antagonistic: http://www.jclinepi.com/article/S0895-4356(16)00147-5/abstract http://www.jclinepi.com/article/S0895-4356(16)00147-5/abstra... What the OP is referring to isn't unique to MDs, it's endemic to much biomedical research today. I blame it on lack of tenure protections and science-as-university-income, which in many cases ultimately stems from indirect costs charged to federal grants, or the current grant system. For me, the concerns mentioned in this thread about scientific research and MD training in particular, bring up bigger issues pertaining to the culture of hierarchy in medicine and its implications for quality of care and competition in service provision and training models.
- wfunction 10y ago
  Interesting, thank you for the link!
rscho 10y ago
Yeah, so... there is abundant literature on quality assessment. A few samples: - deficiencies in the reporting methodology https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3554513/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3554513/ - a bit of incorrect retraction https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3899113/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3899113/ - a fun one on systematic reviews https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4785311/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4785311/ - regarding missing data https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748550/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748550/ - a paywalled abstract about power https://www.ncbi.nlm.nih.gov/pubmed/26677241 https://www.ncbi.nlm.nih.gov/pubmed/26677241 - on sample size calculations https://www.ncbi.nlm.nih.gov/pubmed/25523375 https://www.ncbi.nlm.nih.gov/pubmed/25523375 - aaand a fun one on selection bias https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4566301/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4566301/ All found within 10 minutes... We could go on like that for hours with this sizing contest. I do not expect to convince you. You will, if you put the time and effort into it, find other studies saying the opposite (although being less sexy for publishers, they will be harder to find). If you are somewhat knowledgeable in the field of statistics, please take a look a the numbers, as I am quite sure you will find them appalling (19% of study population missing outcome data and 27.9% underpowered studies, anyone?)

kem 10y ago

I offer this in the interests of maintaining what I see as a useful, if difficult, discussion, not to be antagonistic: http://www.jclinepi.com/article/S0895-4356(16)00147-5/abstract http://www.jclinepi.com/article/S0895-4356(16)00147-5/abstra... What the OP is referring to isn't unique to MDs, it's endemic to much biomedical research today. I blame it on lack of tenure protections and science-as-university-income, which in many cases ultimately stems from indirect costs charged to federal grants, or the current grant system. For me, the concerns mentioned in this thread about scientific research and MD training in particular, bring up bigger issues pertaining to the culture of hierarchy in medicine and its implications for quality of care and competition in service provision and training models.

wfunction 10y ago
Interesting, thank you for the link!

rscho 10y ago

Yeah, so... there is abundant literature on quality assessment. A few samples: - deficiencies in the reporting methodology https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3554513/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3554513/ - a bit of incorrect retraction https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3899113/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3899113/ - a fun one on systematic reviews https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4785311/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4785311/ - regarding missing data https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748550/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748550/ - a paywalled abstract about power https://www.ncbi.nlm.nih.gov/pubmed/26677241 https://www.ncbi.nlm.nih.gov/pubmed/26677241 - on sample size calculations https://www.ncbi.nlm.nih.gov/pubmed/25523375 https://www.ncbi.nlm.nih.gov/pubmed/25523375 - aaand a fun one on selection bias https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4566301/ https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4566301/ All found within 10 minutes... We could go on like that for hours with this sizing contest. I do not expect to convince you. You will, if you put the time and effort into it, find other studies saying the opposite (although being less sexy for publishers, they will be harder to find). If you are somewhat knowledgeable in the field of statistics, please take a look a the numbers, as I am quite sure you will find them appalling (19% of study population missing outcome data and 27.9% underpowered studies, anyone?)