Prediction of severe erectile dysfunction after penile fracture repair: machine learning analysis results from the reconstruction and trauma working group of the society of urological surgery (RAT-SUS)
Serdar Geyik, Ismail Onder Yilmaz, Mehmet Zubaroglu, Mutlu Deger, Rahmi Kavak, Hilmi Sari, Yavuz Onur Danacıoglu, Çaglar Sertkaya, Mehmet Yilmaz, Ibrahim Haciobey, Mustafa Tıpırdamaz, Mehmet Dündar, Mesut Berkan Duran, Can Sinirsiz, Omer Bayrak, Onur Zeytun, Ali Can Albaz, Murat Demir, Yunus Emre Gö · 2025
Read Prediction of severe erectile dysfunction after penile fracture repair: machine learning analysis results from the reconstruction and trauma working group of the society of urological surgery (RAT-SUS) by Serdar Geyik, Ismail Onder Yilmaz, Mehmet Zubaroglu, Mutlu Deger, Rahmi Kavak, Hilmi Sari, Yavuz Onur Danacıoglu, Çaglar Sertkaya, Mehmet Yilmaz, Ibrahim Haciobey, Mustafa Tıpırdamaz, Mehmet Dündar, Mesut Berkan Duran, Can Sinirsiz, Omer Bayrak, Onur Zeytun, Ali Can Albaz, Murat Demir, Yunus Emre Gö (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12710470.txt.
Description
Serdar Geyik leads a large multicenter team training machine learning models to predict severe erection problems after surgical repair of a penile fracture. Their study appeared in two thousand twenty-five. Such dysfunction is a feared outcome of the injury and its repair. Early warning would help counseling and follow-up planning.
Records covered over five hundred patients treated at many urology centers over four years. Men were split by a standard function score six months after surgery. Eleven learning algorithms competed to forecast the severe group. Several resampling tricks handled the lopsided outcome counts. Importance analysis then ranked which patient factors drove the predictions. The authors say the models surface risk patterns that plain statistics miss. The work comes from a national reconstruction and trauma study group. It aims at bedside prediction, not just description.
Catalog record
| CREATOR | Serdar Geyik, Ismail Onder Yilmaz, Mehmet Zubaroglu, Mutlu Deger, Rahmi Kavak, Hilmi Sari, Yavuz Onur Danacıoglu, Çaglar Sertkaya, Mehmet Yilmaz, Ibrahim Haciobey, Mustafa Tıpırdamaz, Mehmet Dündar, Mesut Berkan Duran, Can Sinirsiz, Omer Bayrak, Onur Zeytun, Ali Can Albaz, Murat Demir, Yunus Emre Gö |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 55 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |