Haven't seen that. Share if you can, thx.

Maybe MICHAEL FERRO can with one of his patents involving medical imaging

https://michaelferro.com/automatic-a...inical-images/


Was any of his technology used in the study he may or may not have leveraged Political favors to gain a grant for?

Oh. The MRI study I shared was a different group. As pointed out by another poster, the study that acknowledged funding from Ferro didn't report radiological differences.

Well that's boring


So no comparison to vaccination and non vax?

Your opinion is supported by your background of being intellectually dishonest.

Nope. That's why others think it's a bad study.

I think it's unreported because there was no difference, but that's only my opinion and unsupported.

What’s your solution here?
Presenting a study that Covid gives you neurological damage, but the vaccine does not prevent Covid, so what measures do you suggest people take?

I have no practical suggestions for mitigation

Nothing?
No lockdowns, masks, social distancing..not even hand washing?

Not my bag

I feel like you get paid to just stir up discourse. Srs.

I don't. But that sounds like a good gig

I just see you always present these studies or information about the harmfulness of Covid, yet the only solution you have ever presented is supporting the vaccine.

Never once do you speak about maintaining a healthy lifestyle (which would be the logical solution given the website you are posting on), eating right, sufficient intake of vitamins, exposure to sun, alternative treatments..nothing but doom & gloom about Covid & your only solution is for people to take the vaccine.

I don't think I've ever supported or recommended any vaccine.

...

That's what appears to be difficult for some to understand. I believe it's possible to question and/or challenge polarized conspiracy theories without endorsing, or accepting, an opposing polarized position.

What CT’s exactly?

Pick one.

You and I disagree on fundamental principles of human physiology. I still read your posts and cited references.
.
These topics aren't about individual miscers, to me.

You think God is a CT now?

You only support one side, the pharmaceutical companies.

I don't think I've ever supported or recommended any vaccine.

Any one in particular? Yeah, maybe not....

Vaccines in general though, you absolutely have. Plus you were one of the ones fighting so hard to defend Pfizer that time when their exec was busted by Project Veritas.

No. None of that happened.

Bullshiit.

That's what appears to be difficult for some to understand. I believe it's possible to question and/or challenge polarized conspiracy theories without endorsing, or accepting, an opposing polarized position.

I believe that, although most people would question the sanity of the individual making the claim, it is possible for people to claim they use a drop down menu on a physical label of an individual syringe to select the lot number.

Others suggest scientists physically supervise regression models

Source?

So no source?

Without knowing how they “supervised” their machine learning model and how they programmed the analysis tools it’s impossible to know what exactly they are determining in that study, regardless of how many fancy words they throw around.

“ We examined the predictive capacity of derived imaging markers employing a supervised machine learning approach (Fig. 5).”



We also don’t know what their python code looks like:

“All statistical analyses were conducted in Python 3.9.1”

However, when seeing what types of competing interests there are it can provide insight into what they may have “steered” their program towards concluding

That's how MRI analysis works and the tools are cited.

Supervised machine learning uses labeled (ground truth) data to train the model.

If you want to review their code, it's here:
https://github.com/csi-hamburg/2022_...covid_imaging/

Were these the people “supervising” the machine learning to train the model??

No.

They provide all of the code and data.

Who supervised the training of the machine learning?

Regression is supervised machine learning.

Who created and/or selected the algorithm?

I don't think a script will do it.

But I also don't think the techniques used in this study were exotic or novel. Pretty standard rly.

That was only part of the study anyway, evaluating predictive ability that held up with sensitivity analyses.

from sklearn.linear_model import LogisticRegressionCV
from sklearn.model_selection import cross_validate, cross_val_predict, StratifiedKFold
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
import pingouin as pg

Nothing exotic, custom, or fancy - linear and logistic regression functions from a standard library.

And yet you still haven’t answered the question “who created the algorithms?”

History
This project was started in 2007 as a Google Summer of Code project by David Cournapeau. Later that year, Matthieu Brucher started work on this project as part of his thesis.

In 2010 Fabian Pedregosa, Gael Varoquaux, Alexandre Gramfort and Vincent Michel of INRIA took leadership of the project and made the first public release, February the 1st 2010. Since then, several releases have appeared following a ~ 3-month cycle, and a thriving international community has been leading the development.

Scikit-Learn is a community driven project, however institutional and private grants help to assure its sustainability.

The contributors, developers, and funders are listed on the same page as this info

https://scikit-learn.org/stable/about.html

Source code is available here

https://github.com/scikit-learn/scikit-learn

Not really surprised, such is the life of a beavercel. And I agree….I’d be extremely interested to hear about any conflicting interests he has.




And since I won’t be discussing the code with him due to his insistence on lying about what people say, I guess I’ll just share this with you and others who may be interested.


https://github.com/csi-hamburg/2022_...ediction.ipynb



There are a few potential ways in which this code could output inaccurate data:

1.) Inconsistent or incorrect data preprocessing: The accuracy of a machine learning model is highly dependent on the quality of the input data. If the prediction_df used to create the X and y variables is not properly preprocessed, it may contain missing values, outliers, or other inconsistencies that could lead to inaccurate results.

2.) Insufficient cross-validation: The code uses nested cross-validation to evaluate the model's performance. However, the number of iterations (iterations=100) may not be sufficient to obtain stable and reliable results. Increasing the number of iterations could help improve the accuracy of the output.

3.) Improper parameter search: The code uses logistic regression with elastic net regularization and performs parameter search using LogisticRegressionCV. The choice of parameter values and the range of values to search may not be optimal for the given dataset. Adjusting the parameter search space or using alternative parameter optimization techniques could potentially lead to more accurate results.

4.) Randomness in cross-validation: The StratifiedKFold cross-validation strategy is used with the shuffle=True option and a different random_state value for each iteration. The random splitting of data could introduce variability in the results. To obtain more reliable estimates, you may consider fixing the random_state to a specific value or performing multiple runs with different random seeds and averaging the results.

5.) Overfitting or underfitting: The code does not include any measures to prevent overfitting or underfitting of the model. It is possible that the model is either too complex or too simple for the given data, leading to inaccurate predictions. Regularization techniques, such as adjusting the regularization strength or exploring other models, could help address this issue.

But the data and code are all freely available.

They used an an accurate, but sophisticated sounding, term for their predictive regression. Probably not necessary, but certainly not a gotcha or reason to disregard the results.

If we consider any study using linear regression (by extension ANOVA) as invalid, we don't have much left. Regression is kinda what everyone does.


The code is well organized and runs quick. You can see for yourself if any of these concerns are present in this data set.

Suggesting there is no code to address or assess under/over fitting while also noting their CV is a little sus.

Thanks.
Now that you realize how easy it is to admit a mistake and/or be honest perhaps it’ll be more common in the r/p

And with all that, the source remains to be seen.

Just LOL @ pseudointellectuals.

If that's how you wanna see it, that's cool

(still cringe tho)