"Initially submitted September 24, 2021, accepted for publication January 13, 2022" (header of second figure, the one with red highlight)
That tells me all I need to know about this paper. Even if it's completely correct, it is a snapshot in time that ignores population-level observations through all of 2022, since any revision would have to be submitted for peer review by end of 2021. One would expect fertility and viability data to be on a 40-week lag anyways, so this study needs to be conducted and peer reviewed annually at least.
Tired of "peer reviewed!" being used as a cudgel to push one paper that agrees with the narrative, even though it's already well out of date.
"self-identified females" - so this could include biological males, who can't get pregnant? I wonder how this distorts FR? Were there more trans women in one group over the other?
I think we need a new version of the Devil's Dictionary, and I'd like to start the project. Let me translate for you. “Immunologist working on pregnancy at @ImperialCollege equality and diversity in STEM."
Immunologist: drug pusher, proponent of replacing human immune system with drugs, a person willing to assume that whatever she doesn't know about the immune system isn't worth knowing.
Equality and Diversity in STEM: a critic of the scientific method, logic and reason, advocates of DIE in STEM believe the only source of truth is a consensus of relevant experts, here defined as people who make their living pushing the same superstitions and/or drugs. A person who knows just enough science to be a menace to themselves and others.
Thanks. At this point in the game, my default position is to assume that every one of the regime's narrative-supporting, dissident-voice-destroying positions is a lie.
These people have contempt for the truth...and for us.
Good catch, Gato. You also went the extra mile checking all the stuff about Cox regression. But, as you say, just looking at the raw numbers and seeing that they "flipped" their ratios... tells me everything I need to know.
It would be interesting to see a study comparing/evaluating the quality of research for researchers who list their pronouns on their bios vs. those who do not. Do the pronoun people tend to produce biased research because they have preexisting ideological biases that bleed over into their work?
Statistical analysis is totally over my head (thank you, Gato, I trust you much more than Viki); but "affecting a small group strongly and others little or transiently" appears to me to be precisely what we are seeing in "vaccine" adverse effects.
Also, this study is part of a pattern that I've seen w/ COVID. Whenever real world data doesn't fit the expectation (ACM goes up despite vaccines, masks/lockdowns don't appear to work etc.),someone generates a study to "debunk" whatever we saw in the real world, trying to get us to believe the study over the real world evidence. Does that make sense? In any other context, would you trust a study over the real world? Setting aside problems w/ any particular study, when did studies become more reliable than reality itself? If you stuck your hand on the stove & got burnt, would you care what my study said? To put it another way, the meta-argument here is that vaccines/lockdowns/masks are just super unlucky - there's always some sad coincidence that makes them APPEAR to fail when, in fact, they're working great. But is that a reasonable approach to science?
In the final analysis, aren't we just saying that X worked, but the patient died? If we aren't doing this for the real world effects, why are we doing it? Shouldn't policies/drugs be judged on their real world effects? If people who do X seem to die more than people who don't, would you keep doing X because - in theory - X works? Isn't it more likely that X is to blame in ways we do not understand than X is great, but unlucky? Seems to me that "luck" is apt to be something we don't understand rather than true luck.
So, sure, maybe all these policies/drugs failed for a variety of coincidental reasons or maybe all these policies/drugs failed because they're bad ideas & we just haven't yet figured out why or how they're bad ideas.
It's not even about the burden of proof or heuristics or science or whatever nonsense they're citing; it's about epistemology. Should we ignore what we know (the real world data) to rely upon what we can't possibly know for sure (models, studies etc.)? I'd say no. For example, I trust DNA, but I wouldn't convict somebody based on DNA alone - I'd need corroboration. Wouldn't you? You think those tests are never wrong? Never faked? Wouldn't you want something else linking the accused to the crime? So shouldn't these vaccines - and every other policy - generate some real world corroboration? Do we think it's possible that DNA would be the perp's only link to the crime? Do we think it's possible that these ideas would NEVER generate corroboration? Sure, both are theoretically possible, but would we consider those possibilities reasonable?
At this point, the dearth of real world proof of vaccine efficacy & safety is proof of the opposite.
viki "...pregnancy...equality...diversity..." can't even get the "E" right in DEI, so there's that. And those three words in a self-description also give me paws (intended). But I wonder why she/her didn't include "Inclusion" in her description given the fact the study is counting "male" pregnancies.
It used to be that a requirement for entrance into medical school was demonstrated proficiency in science, including math, physics, chemistry and biology. It now should become a requirement that entrants demonstrate proficiency in fake science, so they can disentangle the brambles of this kind of science from the real science upon which health and medicine ought to be based. Alternatively, those with demonstrated proficiency in this area could find jobs working for big Pharma bamboozling other doctors, regulators, and the public. What a way to make a living!!
Peter has been proven right in the past, but his critics never acknowledge that fact or apologize. Smart people know who to trust. When he has been wrong, he admits it and corrects...for example “one and done”...because he has integrity. These pronoun pushers have none.
Here's just the conclusions section from the article. Pretty damning
Conclusions. All papers based on this code should be retracted immediately. Imperial’s modelling efforts should be reset with a new team that isn’t under Professor Ferguson, and which has a commitment to replicable results with published code from day one.
On a personal level, I’d go further and suggest that all academic epidemiology be defunded. This sort of work is best done by the insurance sector. Insurers employ modellers and data scientists, but also employ managers whose job is to decide whether a model is accurate enough for real world usage and professional software engineers to ensure model software is properly tested, understandable and so on. Academic efforts don’t have these people, and the results speak for themselves.
"Initially submitted September 24, 2021, accepted for publication January 13, 2022" (header of second figure, the one with red highlight)
That tells me all I need to know about this paper. Even if it's completely correct, it is a snapshot in time that ignores population-level observations through all of 2022, since any revision would have to be submitted for peer review by end of 2021. One would expect fertility and viability data to be on a 40-week lag anyways, so this study needs to be conducted and peer reviewed annually at least.
Tired of "peer reviewed!" being used as a cudgel to push one paper that agrees with the narrative, even though it's already well out of date.
"self-identified females" - so this could include biological males, who can't get pregnant? I wonder how this distorts FR? Were there more trans women in one group over the other?
I think we need a new version of the Devil's Dictionary, and I'd like to start the project. Let me translate for you. “Immunologist working on pregnancy at @ImperialCollege equality and diversity in STEM."
Immunologist: drug pusher, proponent of replacing human immune system with drugs, a person willing to assume that whatever she doesn't know about the immune system isn't worth knowing.
Equality and Diversity in STEM: a critic of the scientific method, logic and reason, advocates of DIE in STEM believe the only source of truth is a consensus of relevant experts, here defined as people who make their living pushing the same superstitions and/or drugs. A person who knows just enough science to be a menace to themselves and others.
Thanks. At this point in the game, my default position is to assume that every one of the regime's narrative-supporting, dissident-voice-destroying positions is a lie.
These people have contempt for the truth...and for us.
Good catch, Gato. You also went the extra mile checking all the stuff about Cox regression. But, as you say, just looking at the raw numbers and seeing that they "flipped" their ratios... tells me everything I need to know.
Upside down data, you say? Reminds one of global warming shenanigans. https://climateaudit.org/2009/09/03/kaufmann-and-upside-down-mann/
It would be interesting to see a study comparing/evaluating the quality of research for researchers who list their pronouns on their bios vs. those who do not. Do the pronoun people tend to produce biased research because they have preexisting ideological biases that bleed over into their work?
Statistical analysis is totally over my head (thank you, Gato, I trust you much more than Viki); but "affecting a small group strongly and others little or transiently" appears to me to be precisely what we are seeing in "vaccine" adverse effects.
Also, this study is part of a pattern that I've seen w/ COVID. Whenever real world data doesn't fit the expectation (ACM goes up despite vaccines, masks/lockdowns don't appear to work etc.),someone generates a study to "debunk" whatever we saw in the real world, trying to get us to believe the study over the real world evidence. Does that make sense? In any other context, would you trust a study over the real world? Setting aside problems w/ any particular study, when did studies become more reliable than reality itself? If you stuck your hand on the stove & got burnt, would you care what my study said? To put it another way, the meta-argument here is that vaccines/lockdowns/masks are just super unlucky - there's always some sad coincidence that makes them APPEAR to fail when, in fact, they're working great. But is that a reasonable approach to science?
In the final analysis, aren't we just saying that X worked, but the patient died? If we aren't doing this for the real world effects, why are we doing it? Shouldn't policies/drugs be judged on their real world effects? If people who do X seem to die more than people who don't, would you keep doing X because - in theory - X works? Isn't it more likely that X is to blame in ways we do not understand than X is great, but unlucky? Seems to me that "luck" is apt to be something we don't understand rather than true luck.
So, sure, maybe all these policies/drugs failed for a variety of coincidental reasons or maybe all these policies/drugs failed because they're bad ideas & we just haven't yet figured out why or how they're bad ideas.
It's not even about the burden of proof or heuristics or science or whatever nonsense they're citing; it's about epistemology. Should we ignore what we know (the real world data) to rely upon what we can't possibly know for sure (models, studies etc.)? I'd say no. For example, I trust DNA, but I wouldn't convict somebody based on DNA alone - I'd need corroboration. Wouldn't you? You think those tests are never wrong? Never faked? Wouldn't you want something else linking the accused to the crime? So shouldn't these vaccines - and every other policy - generate some real world corroboration? Do we think it's possible that DNA would be the perp's only link to the crime? Do we think it's possible that these ideas would NEVER generate corroboration? Sure, both are theoretically possible, but would we consider those possibilities reasonable?
At this point, the dearth of real world proof of vaccine efficacy & safety is proof of the opposite.
It takes two to tango. So don't you need to know the vaccination status of both partners?
viki "...pregnancy...equality...diversity..." can't even get the "E" right in DEI, so there's that. And those three words in a self-description also give me paws (intended). But I wonder why she/her didn't include "Inclusion" in her description given the fact the study is counting "male" pregnancies.
Imperial College at it again, maybe she used professor “cheats on wife” random number generator - that he never released the code for last I heard
It used to be that a requirement for entrance into medical school was demonstrated proficiency in science, including math, physics, chemistry and biology. It now should become a requirement that entrants demonstrate proficiency in fake science, so they can disentangle the brambles of this kind of science from the real science upon which health and medicine ought to be based. Alternatively, those with demonstrated proficiency in this area could find jobs working for big Pharma bamboozling other doctors, regulators, and the public. What a way to make a living!!
Peter has been proven right in the past, but his critics never acknowledge that fact or apologize. Smart people know who to trust. When he has been wrong, he admits it and corrects...for example “one and done”...because he has integrity. These pronoun pushers have none.
The rate of male pregnancies seems a bit higher than expected.
Here's just the conclusions section from the article. Pretty damning
Conclusions. All papers based on this code should be retracted immediately. Imperial’s modelling efforts should be reset with a new team that isn’t under Professor Ferguson, and which has a commitment to replicable results with published code from day one.
On a personal level, I’d go further and suggest that all academic epidemiology be defunded. This sort of work is best done by the insurance sector. Insurers employ modellers and data scientists, but also employ managers whose job is to decide whether a model is accurate enough for real world usage and professional software engineers to ensure model software is properly tested, understandable and so on. Academic efforts don’t have these people, and the results speak for themselves.