METHODS: Forty participants were randomly assigned to four groups: control group (CG, n = 10), compound set training group (CSG, n = 10), pyramid set training group (PSG, n = 10), and superset training group (SSG, n = 10). Excluding the CG, the other three groups underwent an 8-week resistance training program, three sessions per week, at 60%-80% of 1RM intensity for 60-90 min per session. Assessments included body composition, physical fitness components, 1RM, isokinetic muscle functions, and biomechanical properties (muscle frequency, stiffness, etc.) of the rectus abdominis and external oblique muscles.
RESULTS: The PSG demonstrated the most significant improvement in relative peak torque during isokinetic testing of the shoulder and knee joints. Compared to the CG, all exercise groups exhibited positive effects on back strength, sprint performance, 1RM, and core muscle biomechanics. Notably, the PSG showed superior enhancement in external oblique stiffness. However, no significant differences were observed among the exercise groups for rectus abdominis biomechanical properties.
DISCUSSION: Structured resistance training effectively improved maximal strength, functional performance, and core muscle biomechanics. The pyramidal training modality conferred specific benefits for isokinetic muscle functions and external oblique stiffness, suggesting its efficacy in enhancing force production capabilities and core stability.
OBJECTIVE: To summarise the protocol and statistical analysis plan for the Mega-ROX HIE trial.
DESIGN SETTING AND PARTICIPANTS: Mega-ROX HIE is an international randomised clinical trial that will be conducted within an overarching 40,000-participant registry-embedded clinical trial comparing conservative and liberal ICU oxygen therapy regimens. We expect to enrol approximately 4000 participants with suspected HIE following a cardiac arrest who are receiving invasive mechanical ventilation in the ICU.
MAIN OUTCOME MEASURES: The primary outcome is in-hospital all-cause mortality up to 90 days from the date of randomisation. Secondary outcomes include duration of survival, duration of mechanical ventilation, ICU length of stay, hospital length of stay, and the proportion of participants discharged home.
RESULTS AND CONCLUSIONS: Mega-ROX HIE will compare the effect of conservative vs. liberal oxygen therapy regimens on day-90 in-hospital mortality in adults in the ICU with suspected HIE following a cardiac arrest. The protocol and planned analyses are reported here to mitigate analysis bias.
TRIAL REGISTRATION: Australian and New Zealand Clinical Trials Registry (ACTRN 12620000391976).
METHODS: To predict CD while prioritizing patient privacy, our study employed data anonymization involved adding Laplace noise to sensitive features like age and gender. The anonymized dataset underwent analysis using a differential privacy (DP) framework to preserve data privacy. DP ensured confidentiality while extracting insights. Compared with Logistic Regression (LR), Gaussian Naïve Bayes (GNB), and Random Forest (RF), the methodology integrated feature selection, statistical analysis, and SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) for interpretability. This approach facilitates transparent and interpretable AI decision-making, aligning with responsible AI development principles. Overall, it combines privacy preservation, interpretability, and ethical considerations for accurate CD predictions.
RESULTS: Our investigations from the DP framework with LR were promising, with an area under curve (AUC) of 0.848 ± 0.03, an accuracy of 0.797 ± 0.02, precision at 0.789 ± 0.02, recall at 0.797 ± 0.02, and an F1 score of 0.787 ± 0.02, with a comparable performance with the non-privacy framework. The SHAP and LIME based results support clinical findings, show a commitment to transparent and interpretable AI decision-making, and aligns with the principles of responsible AI development.
CONCLUSIONS: Our study endorses a novel approach in predicting CD, amalgamating data anonymization, privacy-preserving methods, interpretability tools SHAP, LIME, and ethical considerations. This responsible AI framework ensures accurate predictions, privacy preservation, and user trust, underscoring the significance of comprehensive and transparent ML models in healthcare. Therefore, this research empowers the ability to forecast CD, providing a vital lifeline to millions of CD patients globally and potentially preventing numerous fatalities.