Introduction The use of modified equipment in youth tennis, such as low-compression balls, is widely recommended to optimize learning, participation, and long-term athlete development. However, little is known about the physical demands imposed by these adaptations in under-10 (U10) competitive players, particularly when considering biological maturation. Methods This study analyzed 72 simulated tournament matches involving 19 tennis players (10.17 ± 1.1 years; 14 boys, 5 girls). Each player competed under two conditions: green balls (25% lower compression) and standard balls. External load variables (active time, recoveries, and work-rest ratio) were recorded with WIMU Pro™ inertial devices, while covariates such as maturity offset (PHV), years of practice, physical activity level, jump height, and number of tournaments were considered. Bayesian repeated-measures ANOVA models were applied to assess differences. Results Model comparisons consistently provided anecdotal to moderate evidence in favor of the null hypothesis for ball type, suggesting that any differences in external load between green and standard balls are likely trivial and of limited practical relevance, with wide credible intervals reflecting substantial individual variability. Conversely, biological maturation (PHV) emerged as the strongest predictor of active time and work-rest ratio, with players closer to their PHV showing reduced active time. Moderate to strong Bayes factors, together with credible intervals excluding zero, indicate a robust and meaningful influence of biological maturation on match demands. Discussion These findings suggest that, in U10 tennis, low-compression balls provide a pedagogical advantage without increasing external load. Instead, biological maturation plays a decisive role in modulating match demands. Green balls represent a convenient and developmentally appropriate tool in U10 tennis, but coaches should therefore prioritize maturity-based individualization of training and guiding the transition to standard ball, rather than relying solely on chronological age.

Abstract Stability selection is a versatile framework for structure estimation and variable selection in high-dimensional settings, primarily grounded in frequentist principles. In this paper, we propose an enhanced methodology that integrates Bayesian analysis to refine the inference of selection probabilities within the stability selection framework. Traditional approaches rely on selection frequencies for decision-making, often overlooking domain-specific knowledge. Our methodology uses prior information to derive posterior distributions of selection probabilities, thereby improving both inference and decision-making. We present a two-step process for engaging with domain experts, enabling statisticians to construct prior distributions informed by expert knowledge, while allowing experts to control the weight of their input on the final results. Using posterior distributions, we offer Bayesian credible intervals to quantify uncertainty in the variable selection process. Furthermore, we demonstrate how incorporating prior knowledge improves selection stability by reducing the variance of selection probabilities and how it contributes to the per-family error rate. Our approach preserves the versatility of stability selection and is suitable for a broad range of structure estimation challenges.

Objective: Single- and dual-task tandem gait are key components of the Sport Concussion Assessment Tool 6 and Sport Concussion Office Assessment Tool 6 (SCOAT6) balance assessment. However, the performance criteria for tandem gait remain unclear for male and female healthy athletes, and those with a concussion. This study aimed to establish normative performance outcomes for single- and dual-task tandem gait in healthy male and female athletes, and to compare their performance with SCOAT6 criteria. Design: Cross-sectional. Setting: Interuniversity athletics program. Participants: Healthy interuniversity athletes (N = 103; female = 43). Independent Variables: N/A. Outcome measures: Three trials of single- and dual-task tandem gait—time to completion, motor errors, and cognitive accuracy were obtained. Results: Female athletes were generally slower than male athletes across both task types on average across all three trials, with a difference of 1.6 seconds [90% Credible Interval (CrI) = 0.5 to 2.6, posterior probability above zero seconds (pprob) = 99.3%] for single-task and 2.8 seconds (90% CrI = 0.6-5.1, pprob = 98.1%) for dual-task. This trend persisted when analyzing the fastest single trial, with female athletes exhibiting slower times by 1.4 seconds (90% CrI = 0.2-2.4, pprob = 97.8%) and 2.1 seconds (90% CrI = 0-4.2, pprob = 94.8%) for single- and dual-tasks, respectively. Furthermore, based on SCOAT6 criteria, most athletes were categorized as “very slow” for single-task (>12.5 seconds; females = 88.4%, males = 71.7%) and dual-task conditions (>16.3 seconds; females = 88.4%, males = 76.7%). Conclusions: Healthy male athletes performed single- and dual-task tandem gait faster than female athletes. In addition, most athletes performed below the “normal” ranges according to SCOAT6 criteria, suggesting that normative performance values for tandem gait may need revisiting to enhance clinical interpretation.

Background Abdominal aortic aneurysm (AAA) progression lacks proven medications. This study aimed to indirectly compare common drugs’ effects on AAA growth rate using a network meta-analysis (NMA) of randomized controlled trials (RCTs) and cohort studies, assessing the reliability of evidence. Methods We systematically searched the Cochrane Library, Embase, Web of Science, and PubMed until 5 June 2025. A Bayesian NMA synthesized direct and indirect evidence on drug effects on AAA growth rate, using standardized mean differences (SMD) with credible intervals (CrI). Cohort study results were analyzed separately. Results After screening, 11 RCTs (2,135 subjects) and 13 cohort studies were included. Pooled RCT results showed roxithromycin significantly reduced AAA growth (SMD [95% CrI]: 0.39 [-0.69 to −0.10]). Roxithromycin also demonstrated advantages over amlodipine and doxycycline in indirect comparisons. Propranolol, perindopril, metformin, azithromycin, and ticagrelor showed no significant benefits. Cohort studies linked slower growth to statins and glucose-lowering drugs (insulin, metformin). Conclusion Roxithromycin, statins, and metformin show promise for potentially limiting AAA expansion. However, findings are constrained by methodological limitations (study design, sample size), necessitating future validation via high-quality RCTs.

To investigate the association between dynamic driving pressure (ΔPdyn) and mortality in mechanically ventilated patients with acute brain injury (ABI), and to evaluate whether neurological and pulmonary injury severity modify this relationship. This prespecified secondary analysis of the VENTIBRAIN prospective study (NCT04459884) included mechanically ventilated adult patients with ABI (traumatic brain injury, subarachnoid hemorrhage, intracranial hemorrhage, or ischemic stroke). ΔPdyn was calculated daily over the first 14 days as peak inspiratory pressure minus positive end-expiratory pressure (PEEP). Bayesian joint models evaluated the time-varying association between ΔPdyn and mortality at ICU discharge, hospital discharge, and 6months. Secondary analyses evaluated effect modification by baseline Glasgow Coma Scale (GCS), PaO₂/FiO₂, and ABI subtype. Among 1,555 patients (median age 59 years, 34.5% female), higher time-varying ΔPdyn was associated with increased ICU mortality (hazard ratio [HR], 1.057 per daily 1cmH2O; 95% credible interval, 1.037-1.078; posterior probability of HR > 1, 99.9%). Findings were consistent across all ABI subtypes and were similar for static ΔP (plateau pressure minus PEEP). The association was strongest in patients with severe ABI (GCS ≤ 8) and severe hypoxemia (PaO₂/FiO₂ ≤ 100). Results were robust across all outcome timepoints and multiple sensitivity analyses. Higher time-varying ΔPdyn was associated with increased mortality in this cohort of patients with ABI. Neurological injury severity independently modified the harm from ΔPdyn with a magnitude comparable to severe hypoxemia. Measurement of ΔPdyn may aid risk stratification and ventilation strategies in ABI; future trials should evaluate the effect of reducing ΔPdyn in this population.

Immunological responses to tetanus, diphtheria, pertussis (Tdap) vaccine in Brazilian hematopoietic stem cell transplant recipients.

Abstract Many wildlife monitoring programmes collect annual data on population abundance. The resulting abundance estimates fluctuate over time partly because of true population change and partly because of observation error. These two components of variation can be separated by fitting the estimates to a population dynamics model within a Bayesian state-space modelling framework. By constraining the population trajectory to be biologically realistic, more precise estimates can be obtained. Independent biological knowledge can be incorporated through choice of model structure and by specifying informative prior distributions on demographic parameters. We illustrate the approach using a 31-year point transect study of the Hawai’i ’ākepa (Loxops coccineus). We fitted five models, each making different assumptions about how population change, recruitment and/or adult survival varied over time. Overall, the ’ākepa geometric mean growth rate was 1.02, indicating an increasing population over the 31-year time series, although there were periods of slow decline potentially associated with low recruitment and more rapid recovery associated with pulses of high recruitment. Abundance estimates derived from the population models were substantially more precise than the ‘raw’ point transect estimates: 95% credible interval (CrI) was on average 51.7% (s.d. = 14.1%) narrower.

This study examines the epidemiology of sarcopenic obesity (SO) in China, focusing on national prevalence, modifiable influencing factors, and its longitudinal association with dementia risk in middle-aged and older adults. Using the 2015 wave of the China Health and Retirement Longitudinal Study (CHARLS), SO prevalence was estimated nationally and regionally using Bayesian spatial modeling. Modifiable influencing factors were identified via multivariable logistic regression, with their relative contributions quantified using Extreme Gradient Boosting (XGBoost). The association between baseline SO (2015) and incident dementia risk (2018) was assessed using multivariable logistic regression. Among 10,256 participants aged ≥ 45 years, the national SO prevalence was 6.40% (95% Bayesian confidence intervals [BCIs]: 6.39-6.41%), with significant regional variation (Northern region: 8.60% [8.59-8.61%]; Southern region: 4.90% [4.86-4.94%]). Older age, female sex, hypertension, and depressive symptoms showed statistically significant associations with SO. Age emerged as the strongest predictor in XGBoost analysis. Compared to normal-weight individuals without sarcopenia, those with normal-weight sarcopenia had 63% higher dementia risk (odds ratio [OR] = 1.63; 95% confidence interval [CI]: 1.04-2.58), while SO individuals showed 89% increased risk (OR = 1.89; 95% CI: 1.67-2.15). These findings emphasize the significant geographic disparity in SO prevalence across China and reveal that SO is associated with a substantially elevated risk of dementia, underscoring the need for targeted interventions in aging populations.

Endoscopic submucosal dissection (ESD) is a commonly used minimally invasive technique for treating early esophageal tumors. Postoperative esophageal stricture is a frequent complication. This study aimed to compare the efficacy of existing pharmacological therapies in preventing esophageal stricture after ESD and to determine the most effective regimen to inform clinical practice. A systematic literature search of the Cochrane Library, Embase, PubMed, and Web of Science databases was performed from inception to 20 March 2025. The main search terms included "endoscopic mucosal resection" and "ESD." The main outcome assessed was the incidence of esophageal stricture, while the secondary outcome involved the number of endoscopic balloon dilatation (EBD) sessions needed to manage severe postoperative strictures. This network meta-analysis was conducted using random-effects models. Mean differences (MDs) and relative risks (RRs) were estimated along with their 95% credible intervals (CrIs). Interventions were ranked based on the surface under the cumulative ranking curve (SUCRA) values. All statistical analyses were conducted using R software (version 4.5.0). Thirty-one studies involving 3085 patients who underwent ESD were included. In terms of esophageal stricture incidence, oral hydrocortisone sodium succinate combined with aluminum phosphate gel demonstrated the greatest reduction in the risk of stricture compared with standard care (RR=0.075, 95% CrI: 0.017, 0.24; SUCRA=95.21%). Regarding the number of EBD sessions, the combination of polyglycolic acid (PGA) sheet plus fibrin glue resulted in the fewest sessions compared with standard care (MD=-6.4, 95% CrI:-8.4,-4.3; SUCRA=95.82%). In patients undergoing ESD, oral hydrocortisone sodium succinate combined with aluminum phosphate gel was the most effective intervention for reducing the incidence of postoperative esophageal stricture. The combination of PGA sheet plus fibrin glue was most effective in minimizing the number of EBD sessions required.

Ellipsometric mapping measurements and Bayesian evaluation were performed with a non-collimated, imaging ellipsometer using an LCD monitor as a light source. In such a configuration, the polarization state of the illumination and the local angle of incidence vary spatially and spectrally, rendering conventional spectroscopic ellipsometry inversion methods hardly applicable. To address these limitations, a multilayer optical forward model is augmented with instrument-specific correction parameters describing the polarization state of the monitor and the angle-of-incidence map. These parameters are determined through a Bayesian calibration procedure using well-characterized Si-SiO2 reference wafers. The resulting posterior distribution is explored by global optimization based on simulated annealing, yielding a maximum a posteriori estimate, followed by marginalization to quantify uncertainties and parameter correlations. The calibrated correction parameters are subsequently incorporated as informative priors in the Bayesian analysis of unknown samples, including polycrystalline–silicon layers deposited on Si-SiO2 substrates and additional Si-SiO2 wafers outside the calibration set. The approach allows consistent propagation of calibration uncertainties into the inferred layer parameters and provides credible intervals and correlation information that cannot be obtained from conventional least-squares methods. The results demonstrate that, despite the broadband nature of the RGB measurement and the limited number of analyzer orientations, reliable layer thicknesses can be obtained with quantified uncertainties for a wide range of technologically relevant samples. The proposed Bayesian framework enables a transparent interpretation of the measurement accuracy and limitations, providing a robust basis for large-area ellipsometric mapping of multilayer structures.

This paper introduces a novel extension of the classical Lindley distribution, termed the X-Lindley model, obtained by a specific mixture of exponential and Lindley distributions, thereby substantially enriching the distributional flexibility. To enhance its inferential scope, a comprehensive reliability analysis is developed under a generalized progressive hybrid censoring scheme, which unifies and extends several traditional censoring mechanisms and allows practitioners to accommodate stringent experimental and cost constraints commonly encountered in reliability and life-testing studies. Within this unified censoring framework, likelihood-based estimation procedures for the model parameters and key reliability characteristics are derived. Fisher information is obtained, enabling the establishment of asymptotic properties of the frequentist estimators, including consistency and normality. A Bayesian inferential paradigm using Markov chain Monte Carlo techniques is proposed by assigning a conjugate gamma prior to the model parameter under the squared error loss, yielding point estimates, highest posterior density credible intervals, and posterior reliability summaries with enhanced interpretability. Extensive Monte Carlo simulations, conducted under a broad range of censoring configurations and assessed using four precision-based performance criteria, demonstrate the stability and efficiency of the proposed estimators. The results reveal low bias, reduced mean squared error, and shorter interval lengths for the XLindley parameter estimates, while maintaining accurate coverage probabilities. The practical relevance of the proposed methodology is further illustrated through two real-life data applications from engineering and physical sciences, where the XLindley model provides a markedly improved fit and more realistic reliability assessment. By integrating an innovative lifetime model with a highly flexible censoring strategy and a dual frequentist–Bayesian inferential framework, this study offers a substantive contribution to modern survival theory.

High ambient temperature during pregnancy and offspring cerebral palsy: A population-based case-control study in California.

BackgroundContinuous atrial fibrillation (AF) burden assessment is clinically relevant post-ablation but often limited by the invasiveness of current monitoring tools. Wearable technology may enable real-world AF burden tracking, but evidence in post-ablation patients is limited.ObjectiveTo assess the feasibility of smartwatch-based AF burden quantification after catheter ablation, including its association with quality-of-life.MethodsThis prospective, single-centre study enrolled a convenience sample of patients undergoing AF ablation. Participants used a smartwatch daily for 12 months within a digital follow-up program (daily smartwatch electrocardiogram (ECG) recommended). AF burden was defined as the percentage of monitored days with AF-detected ECGs. A Bayesian multilevel model examined the association between AF burden and quality-of-life score (AFEQT).ResultsTwenty patients (mean age 52.6 ± 10.3 years; 10% female) were enrolled. Over 12 months, 3604 smartwatch ECGs were collected (mean 180 per participant), with AF detected in 55% of patients. Patients submitted an ECG in 36% of days, and the median AF burden was 1.4% (IQR 4.6%), ranging from 0% to 25%. Quality-of-life improved significantly over time: AFEQT score increased 1.64 points per month (95% Credible Interval: 0.77 to 2.51), with 69% of patients achieving a clinically meaningful improvement. In contrast to AF recurrence as a binary variable, higher AF burden was associated with smaller improvements in AFEQT scores in the Bayesian model (interaction estimate: -0.22; 95% Credible Interval: -0.39 to -0.05). Each 1% increase in AF burden corresponded to an estimated 3.8-point reduction in AFEQT at 12 months.ConclusionSmartwatch-based monitoring is a feasible method for estimating AF burden after ablation. Higher AF burden was associated with smaller improvements in Qol, supporting its relevance as a meaningful outcome metric.Remote Monitoring Workflow12-Month Smartwatch AF burden

The hypothesis that urgency, a trait quantifying individual differences in impulsive behaviors driven by intense emotions, moderates associations between affect and alcohol use has received inconsistent support in EMA research. This registered report tested whether trait- and state-level urgency moderate affect-substance use (alcohol and cannabis use) associations in young adults. 496 adults (aged 18-22) completed ecological momentary assessment surveys five times daily across 32 days over eight weekends. Positive affect was associated with increased alcohol use probability, while negative affect was associated with decreased alcohol use probability; cannabis use showed minimal associations with daily affect. Contrary to hypotheses, we found minimal evidence that urgency moderated daily affect-substance use associations. Interaction effects were consistently estimated around the null value with narrow credible intervals. Results challenge theoretical predictions about urgency's role in emotion-driven substance use and support simpler affect-substance use models.

This study presents a Bayesian neural networks framework with LASSO regularization and the GSMeSP interpretability tool, enabling accurate, uncertainty-aware, and biologically interpretable genomic prediction. Deep learning offers significant potential for genomic prediction by modeling complex, nonlinear genotype-phenotype relationships. However, its application in plant breeding has been constrained by limited model interpretability and a lack of uncertainty quantification. To address these challenges, we developed a Bayesian neural networks (BNNs) framework incorporating least absolute shrinkage and selection operator (LASSO) regularization for multi-trait genomic prediction with credible uncertainty estimation. In parallel, we introduce GSMeSP, a novel interpretability framework that integrates SHapley Additive exPlanations (SHAP) with genome-wide association study (GWAS) signals to prioritize trait-associated single nucleotide polymorphisms (SNPs) from both statistical and biological perspectives. We applied this framework to a diverse panel of 1385 upland cotton (Gossypium hirsutum) accessions genotyped with over 12,000 SNPs, evaluating performance across multiple fiber-related traits. The BNNs model consistently outperformed conventional and deep learning benchmarks, achieving 0.46-47.85% improvements in predictive accuracy. Moreover, it generated trait- and sample-specific 95% credible intervals, enabling robust uncertainty quantification and more informed selection decisions. Using GSMeSP, we identified biologically meaningful loci, with a substantial proportion of top-ranked SNPs located in the D-subgenome. Notably, chromosome D05 emerged as a genomic hotspot enriched for SNPs associated with fiber length, lint percentage, and uniformity. By integrating high predictive performance, credible uncertainty estimation, and biologically grounded interpretability, our framework provides a transparent and robust deep learning approach to accelerate genomic selection in crop breeding programs.

Antibiotic use has recently emerged as a potential risk factor for psychiatric disorders, but evidence regarding its risk during pregnancy remains limited, despite the frequent use of antibiotics in pregnant women. We investigated the association between antibiotic use in the year prior to early pregnancy and psychological distress during early- to mid-pregnancy. Participants were 94,490 expectant mothers from the Japan Environment and Children's Study, an ongoing nationwide birth cohort study. Antibiotic use during the year before early pregnancy was categorized as follows: (1) no use, (2) use during either the period before or after pregnancy recognition, and (3) use during both periods. Psychological distress was measured during early- to mid-pregnancy using the Kessler Psychological Distress Scale, with scores of 5-12 and ≥ 13 indicating moderate and severe psychological distress, respectively. A Bayesian multinomial generalized linear model was used to calculate adjusted odds ratios (aORs) and 95% credible intervals (95% CrIs), controlling for a priori selected potential confounders. Analysis using no antibiotic use as a reference revealed that the aORs (95% CrIs) for moderate psychological distress were 1.07 (0.97-1.19) for use during either period and 1.22 (1.08-1.38) for use during both periods. For severe psychological distress, the aORs (95% CrIs) were 1.07 (0.97-1.19) and 1.50 (1.15-1.94), respectively. A dose-response-like pattern was observed, suggesting that even limited antibiotic use may be an independent risk factor for psychological distress during early- to mid-pregnancy, highlighting the importance of judicious antibiotic use from the preconception period.

In reliability theory and engineering applications, the stress-strength model has become a widely used and critically important model. This article studies the reliability modeling and statistical inference of time-dependent consecutive k/n:F systems based on the inverse Weibull distribution. By introducing the strength degradation value of components and the difference between the initial strength and stress of components at time t, time-dependent failure modeling and assessment of the system reliability are performed. Based on the asymptotic likelihood theory and Bayesian statistical methods, the maximum likelihood estimation (MLE), Bayesian estimation, asymptotic confidence interval (ACI), and highest posterior density (HPD) credible interval of the system reliability are obtained. The mean square error (MSE) of the estimations and the coverage probabilities (CP) of the two interval estimation methods are analyzed. The reliability estimates of the system with different values of time-dependent shape parameters are simulated numerically. The average coverage width (AW) of the two interval estimation methods and the coverage probabilities under the Monte Carlo simulation algorithm are compared, and the trend of system reliability with the time parameter t is analyzed. An empirical dataset is analyzed to demonstrate the developed model and the methodology employed.

Objectives This is the first nationwide study focusing exclusively on blind boarding schools in Ethiopia. The study examined the experiences and perceptions of Ethiopian blind boarding school teachers regarding pre-braille skills, unified English braille, and braille (mathematics, technology, usage, and inclusivity). Methods Seven blind boarding schools and their 110 teachers (98.21%) were involved using the census sampling technique. Researchers collected data through questionnaires and analysed it using Bayesian and ordinal logistic regression. Results Teachers were more likely to favour situations with insufficient teaching materials and limited exposure to pre-braille implementation (95% credible interval of OR: [1.138, 1.592]; OR = 1.345). Teachers without visual impairment perceived braille maths content as difficult and lacked access to learn and access mathematics in braille (95% credible interval of OR is [1.075, 2.102]; OR = 1.503). Agreement on the need for special classes in inclusive settings was significantly higher for students with visual impairment (OR = 4.61, p = 0.001). Voice recorders (OR = 4.85, p = 0.003) and text-to-speech software (OR = 3.61, p = 0.001) significantly hindered braille development and adoption. Participants showed slight motivational agreement on technology in braille instruction (OR = 1.09, p = 0.003) and strong agreement regarding improved learning outcomes (OR = 469.21, p = 0.001). Conclusions Further research should investigate UEB awareness and the impact of braille technologies on students with visual impairments’ academic performances. Teachers and curriculum developers should emphasise pre-braille skills, braille maths, braille tech, and transcription services.

We evaluated the clinical and cost effectiveness of an online sleep intervention (COSI) for parents of children with epilepsy. We conducted a multicentre, parallel-group, unblinded, randomised controlled trial. We recruited children aged 4–12 years with epilepsy and sleep problems through 26 UK outpatient clinics. Participants were randomly assigned (1:1) via a computer-generated minimisation algorithm. The primary outcome was the Children’s Sleep Habits Questionnaire (CSHQ) at three months. Cost-effectiveness was estimated at six months. We conducted intention to treat analyses. 85 children were enrolled (42 SC; 43 SC + COSI). At three months, the adjusted mean CSHQ difference between arms was 3.00 (95% CI 0.06–5.93; p = 0.05), indicating significant superiority of SC. Children in the SC + COSI group showed a mean 16.5-minute reduction in sleep onset latency by actigraphy and parents increased their knowledge. Only 23 (53%) families accessed the core intervention materials. Incremental mean cost of SC + COSI was £1,232 (95% credibility interval £535–£3,455) with a mean incremental Quality Adjusted Life Year (QALY) of 0.00 (95% CI -0.03 to 0.04), yielding an incremental cost-effectiveness ratio of £433,167 per QALY gained a (0.04 probability of being cost-effective at the £30,000/QALY threshold). Improved objective sleep onset latency and enhanced parental knowledge suggest that the underlying behaviour change techniques hold value.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-27206-3.

Cesarean section is frequently performed for breech presentation; however, external cephalic version (ECV) is recommended as an alternative strategy to increase the likelihood of vaginal birth. Tocolytics agents are commonly administrated to improve ECV success, yet the comparative effectiveness of different regimes remains inadequately characterized. To systematically evaluate and compare the efficacy and safety of various tocolytic agents in facilitating successful ECV through a Bayesian network meta-analysis. Bayesian network meta-analysis. Bayesian network meta-analysis was performed using the "gemtc" package in R 4.1.1. Treatment effects were quantified by calculating odds ratios (ORs) with corresponding 95% credible intervals (CrIs). Surface under the cumulative ranking curve values were used to rank tocolytic agents according to ECV success rates, maternal outcomes, and adverse events. A total of sixteen RCTs encompassing 2,817 participants and six distinct tocolytic agents met the inclusion criteria. Compared with placebo, terbutaline (OR: 2.7, 95% CrI: 1.1-6.4) and ritodrine (OR: 2.2, 95% CrI: 1.4-3.9) were associated with significantly higher ECV success rates. Additionally, terbutaline was linked to an increased likelihood of vaginal delivery (OR: 2.0, 95% CrI: 1.0-2.9). Maternal adverse effects, including tachycardia, palpitations, hypotension, nausea, dizziness, and flushing, were more frequently reported with terbutaline, nifedipine, and nitroglycerin than with placebo. No statistically significant differences in fetal heart rate abnormalities were detected among the elevated interventions. Terbutaline and ritodrine appear to offer superior efficacy in improving ECV success compared with alternative tocolytic agents, albeit with a higher incidence of maternal side effects. Consequently, clinical decision-making regarding tocolytic use should be informed by a comprehensive assessment of the associated benefits and potential risks.