AI power traction growth prediction model.

bentdiscipline · Tuesday at 4:41 PM

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.

oldandlively · Tuesday at 5:17 PM

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)

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).

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.

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.

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:

Sciences behind PE

We always wonder: "Where in the world do these PE routines got their information from?" "What crackpot sciences, medical, and empirical evidence do they obtain to their info for their PE?" Over the decades of constant researches, PE actually is backed up with science, medical, and clinical...

mattersofsize.com

Feel free to dump them all into a centralized area.

DLD · Tuesday at 7:20 PM

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:

Sciences behind PE

We always wonder: "Where in the world do these PE routines got their information from?" "What crackpot sciences, medical, and empirical evidence do they obtain to their info for their PE?" Over the decades of constant researches, PE actually is backed up with science, medical, and clinical...

mattersofsize.com

Feel free to dump them all into a centralized area.

Excellent post! So much knowledge 🙌

bentdiscipline · 2024-07-03T09:25:02-0400

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

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.

oldandlively · 2024-07-03T14:13:51-0400

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.

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.

bentdiscipline · 2024-07-03T14:49:36-0400

DLD · 2024-07-03T18:00:59-0400

bentdiscipline said:
View attachment 1836501

Great chart!

bentdiscipline · 2024-07-04T15:38:32-0400

If anyone has the paid version of gpt I made this into a gpts found at https://chatgpt.com/g/g-mDwgr6vUz-in-a-pickle