Bibliographic record
Abstract
Introduction: Decision-making for orchiectomy following testicular torsion often relies on subjective clinical evaluations.This study investigated the efficacy of machine-learning (ML) models in objectively predicting post-torsion testicular viability, aiming to maintain a parenchymal ratio over 80% compared to the contralateral testicle, irrespective of initial appearance and surgical timing.Methods: A prospective database from a single surgeon (2020-2024) covering all patients who underwent detorsion and subsequent bilateral orchidopexy was used.Followup ultrasounds were conducted at 6-12 months post-procedure.Variables included patient age (neonatal, prepubertal, post-pubertal), time from presentation to surgery, and history of torsion-detorsion events.Various ML models -regression, k-nearest neighbors, and decision trees -were assessed for accuracy and precision. Results:The decision tree model demonstrated the highest accuracy at 90.5%, followed by the regression model at 82.7% accuracy, and the k-nearest neighbors model at 81.4% accuracy.The area under the curve (AUC) for the regression model indicated adequate predictability for testicular viability.A crucial finding was the impact of the timing of surgical intervention; surgeries conducted within four hours showed a 100% viability rate (p=0.001).Age also significantly affected outcomes, with post-pubertal patients showing a higher viability rate of 57.1% (p=0.041).Detailed performance metrics, such as precision, recall, and F1-scores for each model, further validate the predictive capacity of these ML models (Figure 1, Tables 1, 2).Conclusions: Preliminary results suggest that ML models are viable tools for predicting testicular viability post-torsion, significantly enhancing clinical decisionmaking by providing an objective basis for potentially preserving testicles.Further studies with larger datasets are necessary to confirm and refine these predictions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.314 | 0.111 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".