AI power traction growth prediction model.

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bentdiscipline

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Used AI to do a meta analysis of every study found about traction. The goal was to figure out an accurate prediction of gains. This prediction model is based on traction of the glands with lower forces. (No taping required)

Summary: expect 1” of actual gain (kept even after you stop) from traction every 1270hr to 1650hrs+ depending on your present length (as you get longer it takes more time since it’s a volume change not simply a length change). Newbie gains and losses when not doing traction is the alignment/straightening of tissues fibres vs the reverse when not doing regular traction. This gain or loss is approximately 0.068 x length and the 1.8% circumference change in the opposite direction.
When doing traction Girth will increase eventually proportionally to existing the ratio of length to girth (circumference) of either starting measurements (before ever stretching) or ratio after consistent stretching.

For a typical guy starting at 5.5 inches can expect after 1270hrs to reach 6.9” yet when they stop for a long period of time will come back down to 6.5”. If their circumstances was 4.5” to start it would drop a little at the start of stretching when they get the. Newbie gains in length yet will grow proportionally to be 5.3” after the length comes back to 6.5”.

Ask me any questions you would like about how these calculations were made and what papers I used to feed gpt 4.0 to come up with the final results.
 
bentdiscipline said:
Used AI to do a meta analysis of every study found about traction. The goal was to figure out an accurate prediction of gains. This prediction model is based on traction of the glands with lower forces. (No taping required)
Click to expand...
Unfortunately, AI meta analysis doesn't provide the comprehensive publications of medical, biological, biochemical, pathological, and neurological areas. I've done that too for the past 2 years since AI large language models have become more optimized. AI is lacking the elevated comprehension to compile analytical curve models and cellular degradation-degeneration-necrosis models to compete with cellular regressive-growth-repair models. Oh, we can discuss this at length if you like. I love to have a good meaty discusion enjoyment in this field.

bentdiscipline said:
Summary: expect 1” of actual gain (kept even after you stop) from traction every 1270hr to 1650hrs+ depending on your present length (as you get longer it takes more time since it’s a volume change not simply a length change).
Click to expand...
In reality, through regressive modeling and cellular growth models, while under tension of 1100g to 1300g, it takes around 960 hours for optimal healthy patients to 1160 hours for most average cases, to as long as 1977 hours for regressive patients with low growth potential to gain 0.53in. Outliers where patients with optimal growth conditions due to residual human growth hormones and soft tissue malleability can experience gain up to 2.2in of length with 0.16in of girth in permanent cementation. Due to the heavy outliers and average from corrective curvature to low density-converting-to-high-density reaching plateaus, clinicial reported overly optimal results with the skew to the positive sides where 1in to 1.5in could be gained between 4 to 7 months mark. This is marketing ploys. We can use this as optimistic outlooks, but reality will hit you harder down the road.

bentdiscipline said:
Newbie gains and losses when not doing traction is the alignment/straightening of tissues fibres vs the reverse when not doing regular traction. This gain or loss is approximately 0.068 x length and the 1.8% circumference change in the opposite direction.
When doing traction Girth will increase eventually proportionally to existing the ratio of length to girth (circumference) of either starting measurements (before ever stretching) or ratio after consistent stretching.
Click to expand...
0.068 is a growth factor utilized in regressive-degradation cellular models due to cell necrotitis and cellular stresses during regenerative phasing period for normal post-adulescence finalized growth phases. In reality, the factor is much less once you past 24 years of age. It's around 0.0013. During pre-adulesence, your growth factor is 0.39 between 11 to 19 years of age.

bentdiscipline said:
For a typical guy starting at 5.5 inches can expect after 1270hrs to reach 6.9” yet when they stop for a long period of time will come back down to 6.5”. If their circumstances was 4.5” to start it would drop a little at the start of stretching when they get the. Newbie gains in length yet will grow proportionally to be 5.3” after the length comes back to 6.5”.

Ask me any questions you would like about how these calculations were made and what papers I used to feed gpt 4.0 to come up with the final results.
Click to expand...
GPT 4.0 is just an AI platform to integrate various LLMs. I have three specialized systems with AIs, including GTP 4.0 and 4.0o. I have over 10,000 published articles in my 60TB Raid5 NAS server accessed by my 3 specialized systems with AIs collected in the past 25 years working in my fields of studies. Yes. I would love to discuss with you and everyone on the subject matters. We also started this thread as well:


Feel free to dump them all into a centralized area.
 
oldandlively said:
Unfortunately, AI meta analysis doesn't provide the comprehensive publications of medical, biological, biochemical, pathological, and neurological areas. I've done that too for the past 2 years since AI large language models have become more optimized. AI is lacking the elevated comprehension to compile analytical curve models and cellular degradation-degeneration-necrosis models to compete with cellular regressive-growth-repair models. Oh, we can discuss this at length if you like. I love to have a good meaty discusion enjoyment in this field.


In reality, through regressive modeling and cellular growth models, while under tension of 1100g to 1300g, it takes around 960 hours for optimal healthy patients to 1160 hours for most average cases, to as long as 1977 hours for regressive patients with low growth potential to gain 0.53in. Outliers where patients with optimal growth conditions due to residual human growth hormones and soft tissue malleability can experience gain up to 2.2in of length with 0.16in of girth in permanent cementation. Due to the heavy outliers and average from corrective curvature to low density-converting-to-high-density reaching plateaus, clinicial reported overly optimal results with the skew to the positive sides where 1in to 1.5in could be gained between 4 to 7 months mark. This is marketing ploys. We can use this as optimistic outlooks, but reality will hit you harder down the road.


0.068 is a growth factor utilized in regressive-degradation cellular models due to cell necrotitis and cellular stresses during regenerative phasing period for normal post-adulescence finalized growth phases. In reality, the factor is much less once you past 24 years of age. It's around 0.0013. During pre-adulesence, your growth factor is 0.39 between 11 to 19 years of age.


GPT 4.0 is just an AI platform to integrate various LLMs. I have three specialized systems with AIs, including GTP 4.0 and 4.0o. I have over 10,000 published articles in my 60TB Raid5 NAS server accessed by my 3 specialized systems with AIs collected in the past 25 years working in my fields of studies. Yes. I would love to discuss with you and everyone on the subject matters. We also started this thread as well:


Feel free to dump them all into a centralized area.
Click to expand...
Excellent post! So much knowledge 🙌
 
oldandlively said:
Unfortunately, AI meta analysis doesn't provide the comprehensive publications of medical, biological, biochemical, pathological, and neurological areas. I've done that too for the past 2 years since AI large language models have become more optimized. AI is lacking the elevated comprehension to compile analytical curve models and cellular degradation-degeneration-necrosis models to compete with cellular regressive-growth-repair models. Oh, we can discuss this at length if you like. I love to have a good meaty discusion enjoyment in this field
Click to expand...
Correct! The Meta “research/analysis” was initially done by hand, and only the usable data was provided to build the prediction model (algorithm). GPT-4.0 was used at the time merely to help fit the data backwards. During this process, it was discovered that, in order for the data from multiple studies to make sense, there had to be a missing variable that significantly impacted the results. It turned out that this variable was the starting size of the participants’ members (length and circumference).

With this discovery, a model was built based on the delta volume from traction time. Later, the model needed a constant for the effect of tissue fiber alignment/misalignment (0.068%) [this is not a growth factor, as you assumed]. This constant was again found using the model and back-testing against multiple data sets. This model does not consider outliers and does not attempt to understand the underlying processes; it simply predicts, under normal conditions, the most likely outcome of traction over a given period of time. It also explains newbie gains and shrinkage when not doing regular traction (alignment constant) and why it takes progressively longer to make length gains as the member gets longer.

As the member grows longer, it also grows in circumference, so for each incremental increase in length, a growing increase in cross-sectional profile growth is required. It could be that at higher circumferences, proportionally more traction force is needed to maintain length growth rates, or it could simply be that a volume growth maximum has been reached. This was not possible to determine.

The conclusion of this model are published here anonymously for the benefit of all members of this community and are not connected to any individuals data science or research education. years of experience, accolades or successes nor does the model require justification of its data set size as the model should be able to stand on its on (be proven accurate by backtesting data) without any such a distraction or ego to prove its validity.

“it takes around 960 hours for optimal healthy patients to 1160 hours for most average cases”

The data set you have chosen to quote has already proven this models predictive validity, even without the starting size of the members and the div. when you apply the constant (X1.068) to the average cases (1160 hrs) you are within 2.5% (@1239 hrs) of the predicted 1270 hrs (which is for members starting at 5.4” the avg American size in studies). If the study you pulled your data set from was 5.35” the model lines up perfectly.

Please take this model… attempt to break it and if you can’t keep it as your own, no credit required.
 
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bentdiscipline said:
Correct! The Meta “research/analysis” was initially done by hand, and only the usable data was provided to build the prediction model (algorithm). GPT-4.0 was used at the time merely to help fit the data backwards. During this process, it was discovered that, in order for the data from multiple studies to make sense, there had to be a missing variable that significantly impacted the results. It turned out that this variable was the starting size of the participants’ members (length and circumference).

With this discovery, a model was built based on the delta volume from traction time. Later, the model needed a constant for the effect of tissue fiber alignment/misalignment (0.068%) [this is not a growth factor, as you assumed]. This constant was again found using the model and back-testing against multiple data sets. This model does not consider outliers and does not attempt to understand the underlying processes; it simply predicts, under normal conditions, the most likely outcome of traction over a given period of time. It also explains newbie gains and shrinkage when not doing regular traction (alignment constant) and why it takes progressively longer to make length gains as the member gets longer.

As the member grows longer, it also grows in circumference, so for each incremental increase in length, a growing increase in cross-sectional profile growth is required. It could be that at higher circumferences, proportionally more traction force is needed to maintain length growth rates, or it could simply be that a volume growth maximum has been reached. This was not possible to determine.

The conclusion of this model are published here anonymously for the benefit of all members of this community and are not connected to any individuals data science or research education. years of experience, accolades or successes nor does the model require justification of its data set size as the model should be able to stand on its on (be proven accurate by backtesting data) without any such a distraction or ego to prove its validity.

“it takes around 960 hours for optimal healthy patients to 1160 hours for most average cases”

The data set you have chosen to quote has already proven this models predictive validity, even without the starting size of the members and the div. when you apply the constant (X1.068) to the average cases (1160 hrs) you are within 2.5% (@1239 hrs) of the predicted 1270 hrs (which is for members starting at 5.4” the avg American size in studies). If the study you pulled your data set from was 5.35” the model lines up perfectly.

Please take this model… attempt to break it and if you can’t keep it as your own, no credit required.
Click to expand...
We'll attempt the modeling methodology through other models and compare it, backed by the clinical data results. I believe I have a few charts I made a few years ago on growth analysis from 1200+ patients using the same and similar devices. I'll get back to you on this in the latter days.
 
I have 6 different minds who are interested and using your formulas. They are applying the formulas to various patients with collective of mix data. They'll get back to me down the road.

For me, I'm scratching on your chart. Is the chart based on an individual or a collective of individual? I have a collection of individuals' data and as I plug in the value, some came out correctly, and some skewed negatively.

I have ChatGPT as well. That's my on-the-go AI, beside Google Gemini and experimental Bing.
 
The algorithm requires the starting point of the individual (length and circumference) since this dramatically affects the needed change. The chart was simply one example of a given starting point, which was the average starting dimensions from multiple USA studies. To get a more accurate prediction, try the “In a Pickle” GPT which uses a delta volume and time constant per ml. Enter the length (height) and circumference at the start, then the desired length, and it will automatically calculate the needed change in volume and therefore the time required. In order to access that GPT, you must have the paid version of GPT to get access to the GPTs.

It’s important to keep in mind that this equation accurately predicts the change in volume and time required. This means that when the individual has been actively tractioning, both the ligaments and tissue fibers are as long and therefore as narrow as possible. In this condition, there is a 6.8% increase in length and a slight decrease in circumference (in order for the volume to remain the same). So, this also means that if you are taking results while still regularly tractioning, you will get the “stretched length and stretched width.” When not tractioning for an extended period of time, the length will reduce, and the circumference will increase (final desired / actual values) with the volume remaining static.

Below is the calculation using a constant.
.IMG_0192.png
 
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No worries. All my AI accounts are premium paid for my work, on top of home base AIs for customization for specific tasks.

I'll have my colleagues start this before me. I got multiple priority projects current eating up the AIs queues. Brother SIM or science oriented brothers my try it out and enjoy the verification of the formulas.
 
I noticed that the GPT’s often make calculations errors (since they use 3.5) so it’s probably best if your run this prompt in 4.0 or 4.o

### Required Values:





1. Initial height of the cylinder (in inches): \( h_{initial} \)


2. Initial circumference of the cylinder (in inches): \( C_{initial} \)


3. Desired final height of the cylinder (in inches): \( h_{final} \)





### Calculation





Given:


- \( h_{initial} \)


- \( C_{initial} \)


- \( h_{final} \)





To maintain the same aspect ratio, the new circumference (\( C_{final} \)) can be calculated as:


\[ C_{final} = C_{initial} \times \frac{h_{final}}{h_{initial}} \]





The initial volume of the cylinder in milliliters can be calculated as:


\[ V_{initial} = \pi \times \left(\frac{C_{initial}}{2\pi}\right)^2 \times h_{initial} \times 16.387 \]





The final volume of the cylinder in milliliters can be calculated as:


\[ V_{final} = \pi \times \left(\frac{C_{final}}{2\pi}\right)^2 \times h_{final} \times 16.387 \]





The change in volume is:


\[ \Delta V = V_{final} - V_{initial} \]





The formula to calculate the total time required for the growth is:


\[ T_{total} = 14.163 \, \text{hrs} \times \Delta V \]





Finally, the stretched height can be calculated as:


\[ \text{Stretched Height} = h_{final} \times 1.068 \]





The stretched circumference can be calculated as:


\[ \text{Stretched Circumference} = C_{final} \times 0.937 \]





### Example Calculation





1. Calculate the new circumference:


\[ C_{final} = C_{initial} \times \frac{h_{final}}{h_{initial}} \]





2. Calculate the initial volume:


\[ V_{initial} = \pi \times \left(\frac{C_{initial}}{2\pi}\right)^2 \times h_{initial} \times 16.387 \]





3. Calculate the final volume:


\[ V_{final} = \pi \times \left(\frac{C_{final}}{2\pi}\right)^2 \times h_{final} \times 16.387 \]





4. Calculate the change in volume:


\[ \Delta V = V_{final} - V_{initial} \]





5. Calculate the total time required for growth:


\[ T_{total} = 14.163 \, \text{hrs} \times \Delta V \]





6. Compute the stretched height:


\[ \text{Stretched Height} = h_{final} \times 1.068 \]





7. Compute the stretched circumference:


\[ \text{Stretched Circumference} = C_{final} \times 0.937 \]





### Output





Please provide the values for \( h_{initial} \), \( C_{initial} \), and \( h_{final} \) to receive the calculation results, including the new circumference after growth, the stretched circumference, and the total volume in milliliters.





Once I provide the initial height, initial circumference, and desired final height, I will calculate the total time required for the cylinder to grow, the height when stretched, the new circumference, the circumference when stretched, and the total volume in milliliters for you. It is very important to show the total time required to grow first in the summary.





---
 
bentdiscipline said:
If anyone has the paid version of gpt I made this into a gpts found at https://chatgpt.com/g/g-mDwgr6vUz-in-a-pickleView attachment 1836506View attachment 1836507View attachment 1836508
Click to expand...

"In a Pickle" plugin could not be found in ChatGPT's plugins. My colleagues and I started to create our own spreadsheets and feed the info into the equations. So far, the equations are ~80% accurate with ~20% outliers based on real patient's stats due to predictive plateaus. They are pulling in more info from other stat feeders to get more real time feeds and generate a realtime statistical model for the patients.

Their hopes ran pretty high even with 20% outliers. Patients were being quite positive in seeing their potential growth. One of my colleagues is playing with breast pumping and clitoris predictive models using your equations. That didn't go too well for breasts. But for clitoris, it's about 90% accurate because of the unrestrictive malleability for elogation and expansion. And yes, some clitorises are as long and large as average penises. The largest is 5in x 4in and still going. I short circuited after seeing the message.
 
Got some reported calculation in based on a 25 patient's data collected for the past 15 years.

We are still playing with the first 5 formulas to verify the data. A bit tricky to use macros on the spreadsheets since GPT4s are having problem (spitting errors) using the formulas in series.

This is roughly 90% accurate with normal traction devices. Unfortunately, it doesn't apply too well to those who use other devices, ranging from hanging, force stretches by hands or device assisted, or pumping.

1720571725943.png

This is closely mirroring those who use traction devices for 6 hours a day, 4 days continuous and 1 day off.

1720571468219.png
However, based on centric data of those who wore 14 hours a day, 5 days of continuous with 1 day off, the value is "1.133". Those who use Length Master or equivalent and in combination with pumping and other routines, the value is "1.217". This is where the ~20% outliers.
 
Thank you for your responses and for testing out the model.

in a pickle is a private app and you must follow the link given above to access it.

Happy the model is showing predictive results for such a high percentage,

Never thought of using that model on other body parts very cool.

For the circumference outliers it makes sense since the vaccum is causing additional interstitial fluid accumulation if pumping was stopped for an extended period of time the “non stretched” dimensions would likely be accurate and if only stretching (not vacuum) was applied for a period of time the reduction in circumference should also appear as the model predicts. Basically vacuum pumping is giving false positives of actual circumference growth and can only be accurately measured when not being used for a period of time.

It is fundamental to the model that circumference responds in a negative way durn initial regular stretching since the calculation are based on volume growth and this is a zero some game, if you gain in length you must also loose a little in girth.

I have no scientific studies/evidence to point to yet I believe that stretching is stretching regardless it it’s lengthwise or omni directional (vacuum) the tissues/organ can only respond by growing proportionally (length and circumference). I do not believe that only one dimension can be targeted for growth without the other coming along for the ride.
 
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oldandlively said:
Got some reported calculation in based on a 25 patient's data collected for the past 15 years.

We are still playing with the first 5 formulas to verify the data. A bit tricky to use macros on the spreadsheets since GPT4s are having problem (spitting errors) using the formulas in series.

This is roughly 90% accurate with normal traction devices. Unfortunately, it doesn't apply too well to those who use other devices, ranging from hanging, force stretches by hands or device assisted, or pumping.

View attachment 1836556

This is closely mirroring those who use traction devices for 6 hours a day, 4 days continuous and 1 day off.

View attachment 1836555
However, based on centric data of those who wore 14 hours a day, 5 days of continuous with 1 day off, the value is "1.133". Those who use LengthMaster or equivalent and in combination with pumping and other routines, the value is "1.217". This is where the ~20% outliers.
Click to expand...
Wow chat gtb4 is very advanced!
 
Gpt is just an another tool, without a clear understanding of what you are trying to accomplish and what is a reasonable response it’s no better than attempting to use AutoCAD without a fundamental understanding of drafting.
 
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