Differences in structural connectivity between diabetic and psychological erectile dysfunction revealed by network-based statistic: A diffusion tensor imaging study

Efficacy of acupuncture for psychogenic erectile dysfunction: a randomized, sham-controlled trial

BACKGROUND

Psychogenic erectile dysfunction (pED) is a common sexual dysfunction often accompanied by psychosomatic factors. Its treatment includes oral medications, psychotherapy, and behavioral therapy. Acupuncture's effect on erectile function in pED patients remains to be investigated. This randomized study evaluated the effects of acupuncture and sham acupuncture in pED patients. Altogether, 66 men with pED were randomized to the acupuncture (n = 33) or sham acupuncture group (n = 33). Both groups have a 6-week treatment with 18 sessions. Primary outcome was the International Index of Erectile Function-5 (IIEF-5) at 6 weeks. Secondary outcomes were IIEF-5 (weeks 2, 4, and 10), erection hardness score (EHS), sexual encounter profile-2 (SEP-2), SEP-3, self-rating anxiety scale (SAS), and self-rating depression scale (SDS).

RESULTS

Among the 66 participants, 64 completed the outcome measurements at week 10. Both acupuncture and sham acupuncture groups had improved IIEF-5 and EHS and decreased SAS and SDS post-treatment (p < 0.05). The acupuncture group had significantly better improvement in IIEF-5, EHS, and SEP-3 and significantly reduced SAS and SDS than the sham acupuncture group (p < 0.05). The improvement in SEP-2 post-treatment was not significantly different between the two groups (p > 0.05). There were no serious adverse events.

CONCLUSIONS

The 6-week acupuncture treatment significantly improved the erectile capacity and psychosomatic status of pED patients.

TRIAL REGISTRATION

ChiCTR2200064345 (Chinese Clinical Trial Registry) ( https://www.chictr.org.cn/showproj.html?proj=174873 ).

Computation of the distribution of model accuracy statistics in machine learning: Comparison between analytically derived distributions and simulation-based methods

Background and Aims

All fields have seen an increase in machine-learning techniques. To accurately evaluate the efficacy of novel modeling methods, it is necessary to conduct a critical evaluation of the utilized model metrics, such as sensitivity, specificity, and area under the receiver operator characteristic curve (AUROC). For commonly used model metrics, we proposed the use of analytically derived distributions (ADDs) and compared it with simulation-based approaches.

Methods

A retrospective cohort study was conducted using the England National Health Services Heart Disease Prediction Cohort. Four machine learning models (XGBoost, Random Forest, Artificial Neural Network, and Adaptive Boost) were used. The distribution of the model metrics and covariate gain statistics were empirically derived using boot-strap simulation (N = 10,000). The ADDs were created from analytic formulas from the covariates to describe the distribution of the model metrics and compared with those of bootstrap simulation.

Results

XGBoost had the most optimal model having the highest AUROC and the highest aggregate score considering six other model metrics. Based on the Anderson-Darling test, the distribution of the model metrics created from bootstrap did not significantly deviate from a normal distribution. The variance created from the ADD led to smaller SDs than those derived from bootstrap simulation, whereas the rest of the distribution remained not statistically significantly different.

Conclusions

ADD allows for cross study comparison of model metrics, which is usually done with bootstrapping that rely on simulations, which cannot be replicated by the reader.