A Web-Based Prediction Tool to Improve Identification of Patients With Undersized Hamstring Tendon Autograft in Anterior Cruciate Ligament Reconstruction.

BACKGROUND: An undersized hamstring tendon (HT) autograft is significantly associated with a higher graft failure rate in anterior cruciate ligament reconstruction (ACLR) surgery. The ability to accurately predict inadequate HT graft diameter is critical, as it could assist surgeons in making better graft choices and surgical plans. PURPOSE: To develop a web-based prediction tool to better assess the size of HT autograft and to help clinicians accurately identify patients with potentially undersized HT grafts in order to make appropriate clinical decisions. STUDY DESIGN: Cross-sectional study; Level of evidence, 3. METHODS: A total of 588 patients who received primary arthroscopic single-bundle ACLR surgery with gracilis tendon (GT) and semitendinosus tendon (ST) autograft were retrospectively reviewed. According to the size of 4-strand HT graft, patients were divided into diameter ≥8 mm and <8 mm groups. The least absolute shrinkage and selection operator method and logistic regression were used to identify the independent factors associated with HT graft diameter and establish the models. The prediction performance of the model was evaluated by concordance index and calibration combined with external validation. The diagnostic performance of the prediction model was assessed by sensitivity, specificity, predictive values, and likelihood ratios. Decision curve analysis was used to evaluate the clinical utility of the model. RESULTS: Among the numerous indicators, sex, weight, height, thigh length, and ST-GT diameter (measured on plane 1 of a magnetic resonance imaging scan) were identified to be highly correlated predictors that could provide satisfactory prediction performance in determining the HT graft diameter. Based on these predictors, a prediction model named the HTD model was developed with satisfactory discrimination (concordance index, 0.932) and calibration (mean absolute error, 0.039). When the probability calculated by the HTD model was >65%, the sensitivity and specificity of predicting 4-strand HT graft diameter ≥8 mm were 86.7% and 90.2%, respectively. CONCLUSION: As a useful supplementary prediction tool, the HTD model could accurately predict the diameter of HT autograft during preoperative planning.

Using AI chatbots to provide self-help depression interventions for university students: A randomized trial of effectiveness.

BACKGROUND: Depression impacts the lives of a large number of university students. Mobile-based therapy chatbots are increasingly being used to help young adults who suffer from depression. However, previous trials have short follow-up periods. Evidence of effectiveness in pragmatic conditions are still in lack. OBJECTIVE: This study aimed to compare chatbot therapy to bibliotherapy, which is a widely accepted and proven-useful self-help psychological intervention. The main objective of this study is to add to the evidence of effectiveness for chatbot therapy as a convenient, affordable, interactive self-help intervention for depression. METHODS: An unblinded randomized controlled trial with 83 university students was conducted. The participants were randomly assigned to either a chatbot test group (n = 41) to receive a newly developed chatbot-delivered intervention, or a bibliotherapy control group (n = 42) to receive a minimal level of bibliotherapy. A set of questionnaires was implemented as measurements of clinical variables at baseline and every 4 weeks for a period of 16 weeks, which included the Patient Health Questionnaire-9 (PHQ-9), the Generalized Anxiety Disorder scale (GAD-7), the Positive and Negative Affect Scale (PANAS). The Client Satisfaction Questionnaire-8 (CSQ-8) and the Working Alliance Inventory-Short Revised (WAI-SR) were used to measure satisfaction and therapeutic alliance after the intervention. Participants' self-reported adherence and feedback on the therapy chatbot were also collected. RESULTS: Participants were all university students (undergraduate students (n = 31), postgraduate students (n = 52)). They were between 19 and 28 years old (mean = 23.08, standard deviation (SD) = 1.76) and 55.42% (46/83) female. 24.07% (20/83) participants were lost to follow-up. No significant group difference was found at baseline. In the intention-to-treat analysis, individuals in the chatbot test group showed a significant reduction in the PHQ-9 scores (F = 22.89; P < 0.01) and the GAD-7 scores (F = 5.37; P = 0.02). Follow-up analysis of completers suggested that the reduction of anxiety was significant only in the first 4 weeks. The WAI-SR scores in the chatbot group were higher compared to the bibliotherapy group (t = 7.29; P < 0.01). User feedback showed that process factors were more influential than the content factors. CONCLUSIONS: The chatbot-delivered self-help depression intervention was proven to be superior to the minimal level of bibliotherapy in terms of reduction on depression, anxiety, and therapeutic alliance achieved with participants.

Testing the Value of Probability Forecasts for Calibrated Combining.

We combine the probability forecasts of a real GDP decline from the U.S. Survey of Professional Forecasters, after trimming the forecasts that do not have "value", as measured by the Kuiper Skill Score and in the sense of Merton (1981). For this purpose, we use a simple test to evaluate the probability forecasts. The proposed test does not require the probabilities to be converted to binary forecasts before testing, and it accommodates serial correlation and skewness in the forecasts. We find that the number of forecasters making valuable forecasts decreases sharply as the horizon increases. The beta-transformed linear pool combination scheme, based on the valuable individual forecasts, is shown to outperform the simple average for all horizons on a number of performance measures, including calibration and sharpness. The test helps to identify the good forecasters ex ante, and therefore contributes to the accuracy of the combined forecasts.