Search Results (506)

Cybersecurity risk is commonly expressed through impact and likelihood, yet likelihood remains difficult to estimate because cyber incidents are underreported, heterogeneous datasets are weakly comparable, and attacker behavior changes faster than conventional probability baselines. This article proposes a method for operationalizing likelihood through a cyber exposure profile that integrates external cyber knowledge and organization-specific telemetry into a graph-based representation. The contribution is a formally specified artifact chain—from unified data model through organization-specific profiling, metric registry, likelihood scoring, and control prioritization—that operationalizes four constructs grounded in incident evidence: exposure, traceability, motivation, and systems update. The pipeline provides a pathway from heterogeneous source evidence to a bounded likelihood indicator comparable across organizations and observation periods. An evaluation in 15 real organizations shows that those implementing the cyber exposure profile were associated with reduced incident frequency and faster detection and response times, providing preliminary empirical support for the framework’s directional claims. Full article

The present paper considers an empirical likelihood approach for testing equality of autocorrelation coefficients in two independent stationary time series. In the time domain, a two-sample blockwise empirical likelihood method is constructed for weakly dependent data. In the frequency domain, a two-sample frequency-domain empirical likelihood test is introduced using spectral moment restrictions for autocorrelation. Under suitable regularity conditions, the corresponding profiled empirical likelihood statistics converge to chi-square limits under the null hypothesis. To improve small-sample performance, a bootstrap Bartlett-type calibration is proposed for the profiled two-sample frequency-domain statistic. The finite-sample behavior of the proposed procedures is examined in a Monte Carlo study covering AR, ARMA, and ARFIMA models with both Gaussian and asymmetric heavy-tailed innovations. The results show that the frequency-domain empirical likelihood procedure provides reliable size control in the short-memory models considered and remains competitive in mild long-memory settings, while the benchmark procedures are more sensitive to parametric misspecification or block-length choice. The simulation study shows that the bootstrap Bartlett-type calibration improves performance in smaller samples. An empirical application to squared Nikkei 225 returns provides evidence of higher short-run volatility persistence during the COVID-19 regime than in the pre-pandemic period. Full article

This study investigates the “competence–performance gap” between cognitive ability (measured by the WISC-IV) and actual adaptive performance (measured by the ABAS-II) in children with autism spectrum disorder (ASD), and examines the moderating role of family environment, specifically parental education levels. We applied Latent Profile Analysis (LPA) to cross-sectional data from 3246 children with ASD (aged 6–16 years). The analysis identified three distinct cognitive–adaptive subgroups: the Balanced High-Functioning group (33%), the Classic Mismatch group (44%), and the Cognitively Vulnerable group (23%). Notably, the Classic Mismatch group was characterized by adaptive performance that significantly trailed cognitive potential. Multinomial logistic regression revealed that maternal education—but not paternal education—significantly predicted a child’s likelihood of being in the “Balanced High-Functioning” group. This moderating effect was especially pronounced during the school-age years. These findings highlight the critical role of environmental factors in the translation of intellectual potential into practical social adaptive functioning, providing theoretical support for targeted family-based interventions. Full article

(1) Background: Antibiotic co-administration during COVID-19 hospitalization is common, but evidence supporting routine use without confirmed bacterial co-infection is limited, and the impact on post-COVID recovery remains largely uninvestigated; (2) Methods: Single-center prospective observational cohort of 127 hospitalized COVID-19 adults (March 2020–December 2024) across four pandemic waves. Antibiotic exposure was the primary variable. Endpoints were 30-day mortality, ICU admission, and persistent dyspnea at three months. Multivariable logistic regression with Firth’s penalized profile likelihood 95% CI was performed; ROC analysis assessed procalcitonin (PCT) discrimination; (3) Results: Of 127 patients (median age 70.3 years; 63.8% male; 61.4% unvaccinated), 68 (53.5%) received antibiotics. Notably, 61.5% of patients with PCT ≤ 0.25 ng/mL (viral etiology likely) received antibiotics. After adjustment, antibiotic use was not independently associated with 30-day mortality (OR 0.98, 95% CI 0.27–4.05), ICU admission (OR 1.12, 95% CI 0.31–4.05), or persistent dyspnea at three months (OR 1.51, 95% CI 0.62–4.16). COVID-19 severity was the sole independent mortality predictor (OR 3.563, p = 0.018). At three months, 35.6% reported persistent dyspnea and 14.4% had CT pulmonary fibrosis; (4) Conclusions: Antibiotic exposure did not independently predict short- or long-term outcomes after adjustment for severity, while prescribing was misaligned with PCT-based bacterial probability—supporting biomarker-guided stewardship in epidemic respiratory disease. Full article

The integration of heterogeneous healthcare data sources remains a major challenge in developing reliable and personalized predictive systems for digital healthcare applications. Traditional machine learning methods perform well on structured clinical data but often fail to effectively exploit multimodal information, while deep learning approaches may suffer from instability, weak generalization, and poor calibration when dealing with limited modalities. To address these limitations, this study proposes HA-PI-MADT, a hybrid adaptive healthcare-informed multimodal digital twin-inspired framework that combines deep multimodal representation learning with ensemble-based predictive modeling for robust and trustworthy healthcare prediction. The proposed framework integrates wearable sensor signals, electronic health records (EHRs), CT/MRI imaging representations, and population-level risk prototypes derived from the UCI diabetes dataset within a unified multimodal healthcare representation architecture. In addition, a modality-aware adaptive fusion mechanism dynamically adjusts the contribution of each modality according to its relevance and data quality, while a hybrid stacking strategy combines deep multimodal embeddings with classical ensemble learners to improve predictive robustness and ranking performance. To enhance clinical trustworthiness, calibration-aware optimization is incorporated to improve probabilistic reliability and uncertainty estimation. Extensive experiments conducted on a multimodal healthcare dataset demonstrate that HA-PI-MADT achieves a balanced performance profile across discrimination, ranking, and calibration-oriented evaluation metrics compared with several unimodal, multimodal, and ensemble baselines. The proposed framework achieves strong ranking-oriented and classification performance, including the highest AUPRC (0.6388) and F1-score (0.6327), while also demonstrating competitive calibration-oriented reliability through lower Brier score and negative log-likelihood values. The results demonstrate the effectiveness of the proposed hybrid adaptive multimodal digital twin-inspired framework for reliable, robust, and clinically trustworthy healthcare prediction. Full article

Background: Escherichia coli remains a critical multidrug-resistant nosocomial pathogen, driving interest in bacteriophage-based biocontrol. The genus Kayfunavirus (family Autotranscriptaviridae) exhibits obligately lytic replication cycles and favorable biosafety profiles, yet each new phage requires comprehensive genomic characterization to expand therapeutic candidate pools. This study aimed to isolate and genomically characterize a novel Kayfunavirus from an environmental reservoir in Vietnam. Methods: Escherichia phage vB_EcoA-Sparklingdew was isolated from Can Tho River water using host E. coli AgE9. The genome was assembled using SPAdes. The termini were resolved with PhageTerm. The annotation was done via the Pharokka pipeline and HHpred. Taxonomic classification was performed using taxMyPhage, VIRIDIC intergenomic comparisons, and maximum likelihood phylogeny of concatenated structural proteins. Results: The complete genome comprises a 37,944 bp linear dsDNA molecule (49.9% GC), encoding 51 open reading frames in a predominantly unidirectional arrangement. Key features include a virion-encoded T7-like RNA polymerase, a 723-residue T7-like DNA polymerase, a canonical lysis triad, and two putative tailspike proteins. A 212 bp direct terminal repeat and coverage profiles support a headful (pac) packaging mechanism. Comprehensive screening confirmed the absence of lysogeny, virulence, and antibiotic resistance determinants. A single synonymous SNP indicated high clonal purity. Intergenomic identity peaked at 87.7% against ICTV references, confirming placement in a novel species. Conclusions: Phage Sparklingdew represents a strictly lytic Kayfunavirus with a compact genomic architecture. Its favorable safety profile and absence of temperate markers support further evaluation for targeted therapeutic applications against pathogenic E. coli. Full article

Introduction: Advances in pediatric oncology have transformed cancer into a condition with chronic and long-term developmental consequences. While survival rates have improved significantly, the literature on psychosocial outcomes remains fragmented and inconsistent, with a notable lack of person-centered analyses that account for the heterogeneity of adaptive trajectories. Current evidence fails to explain why survivors with similar clinical profiles exhibit divergent psychological phenotypes, particularly regarding the late effects of multimodal treatments. The aim of this study was to identify heterogeneous psychosocial profiles among adolescent cancer survivors and to examine their associations with treatment complexity and quality of life. Materials and Methods: This cross-sectional study included 165 adolescents aged 12–18 years (mean age: 14.64 years) who were in clinical remission following oncological treatment. Standardized assessment tools were used: the Children’s Depression Inventory 2 (CDI-2™) to measure depressive symptoms, the KIDSCREEN-10 index to assess health-related quality of life (HRQoL), and a scale evaluating satisfaction across 14 life domains. Adaptive profiles were identified using a Two-Stage Cluster Procedure, and risk factors were examined using multinomial logistic regression. Results: Four clusters were identified in the study population: a depressive–dysphoric profile, an anhedonic-withdrawn profile, a highly adaptive profile, and a mixed (struggling) profile. Treatment complexity was identified as a significant independent predictor of membership in the high-distress (depressive) cluster. While each additional therapeutic modality beyond standard chemotherapy was associated with a markedly increased risk (OR = 8.91; p < 0.001), the relatively wide confidence interval (95% CI: 3.27–24.31) suggests that the exact magnitude of this effect should be interpreted with caution. The high lower bound of the interval (3.27), however, strongly supports the directional association of cumulative iatrogenic burden with psychological adaptation. Subjective quality of life functioned as a protective factor against depressive symptoms (OR = 0.57); however, paradoxically, higher self-reported quality of life increased the likelihood of classification into the anhedonic group (OR = 1.81). This divergence between high self-reported HRQoL and social withdrawal potentially suggests a ‘well-being paradox’. It is hypothesized that standard HRQoL instruments may primarily capture physical remission and relief from acute somatic symptoms, potentially masking underlying social–emotional deficits. This suggests that HRQoL scores in survivors should be interpreted with caution and complemented by specific affective screenings. Conclusions: The absence of a uniform pattern of psychological response to cancer among adolescent survivors supports the validity of a patient-centered approach. The burden associated with intensive multimodal treatment significantly increases the likelihood of full-syndrome depression during adolescence. Moreover, the identification of a cluster suggestive of anhedonic and socially withdrawn features highlights the limitations of standard screening tools focused solely on the detection of overt sadness. This heterogeneity underscores the need for personalized psycho-oncological care and the implementation of intensified monitoring for patients at high medical risk. Full article

Background: While maternal obesity is a well-established risk factor for labor induction failure, the specific impact of regional fat distribution and its direct influence on prostaglandin dose–response profiles remain under-investigated. Relying solely on generalized anthropometric metrics like Body Mass Index (BMI) may obscure the true physiological variations in tissue bioavailability. Therefore, this study aimed to compare dinoprostone-induced labor outcomes between obese pregnant women and non-obese controls, and to evaluate whether ultrasonographically measured abdominal subcutaneous fat thickness (ASFT) can serve as a more precise, independent predictor of induction success and cumulative prostaglandin dose requirements. Methods: This prospective two-center observational study was conducted with 200 single-term pregnant women, comprising an obese study group (n = 100, BMI ≥ 30 kg/m²) and a symmetrical non-obese control group (n = 100, BMI 18.5–29.9 kg/m²). Maternal ASFT was measured via high-resolution ultrasonography at the infraumbilical midline prior to labor induction. All participants initially received a 10 mg dinoprostone vaginal insert governed by a standardized institutional induction protocol. Primary outcomes included successful labor induction (defined as achieving vaginal delivery within 24 h) and cumulative prostaglandin dose requirements. Secondary analyses involved comparative evaluation stratified by obesity classes (Class 1 vs. Class 2–3) and an exploratory ASFT threshold (<30 mm vs. ≥30 mm). Results: Obese women demonstrated significantly lower successful labor induction rates (defined as vaginal delivery within 24 h) compared to the non-obese control group (59.0% vs. 86.0%, p = 0.001). The cumulative dinoprostone dose required for cervical ripening was significantly higher in the obese cohort than in controls (16.8 ± 4.5 mg vs. 12.4 ± 2.2 mg, p < 0.001). Similarly, induction-to-delivery intervals were significantly prolonged in obese parturients (19.2 ± 6.1 vs. 14.6 ± 4.8 h, p < 0.001). Subgroup analysis within the obese cohort revealed that patients with ASFT ≥ 30 mm required higher prostaglandin doses (18.08 ± 3.40 mg vs. 14.14 ± 3.08 mg, p < 0.001) and exhibited lower vaginal delivery rates (45.7% vs. 70.4%, p = 0.012) than those with ASFT < 30 mm. Multivariate logistic regression analysis encompassing the entire study population (n = 200) confirmed that ASFT remained a strong independent predictor of induction failure (Adjusted OR: 1.14; 95% CI: 1.05–1.24, p = 0.002). Conversely, generalized obesity metrics via BMI did not maintain independent significance in the multivariate model (p = 0.420). Conclusions: Increased maternal ASFT is directly associated with blunted drug responsiveness, higher cumulative dinoprostone requirements, and a lower likelihood of successful labor induction. Compared with generalized metrics such as BMI, ultrasonographic measurement of ASFT provides a more precise and clinically relevant assessment of regional maternal adiposity. These findings suggest that incorporating pre-induction ASFT evaluation into routine obstetric practice could improve risk stratification and help clinicians design individualized, precision-based dosing strategies for obese parturients. Full article

Background/Objectives: Integrating multi-omics data for cancer prognosis remains a challenging problem in bioinformatics because molecular profiles are high-dimensional, heterogeneous, and structured by incomplete biological relationships. Pathway databases provide biologically meaningful prior knowledge for modeling gene-level associations, but the sparsity and local incompleteness of pathway-derived networks often limit the performance of graph-based survival models. This study aimed to develop a pathway-guided framework for improving multi-omics survival prediction and identifying biologically relevant prognostic signals. Methods: We proposed PANA-Surv, a pathway-guided adaptive neighborhood augmentation framework for multi-omics cancer survival analysis. In this framework, KEGG pathways were used to construct gene graphs, and gene-level multi-omics profiles were encoded as node features. A conditional variational autoencoder module (PANA-VAE) was designed to enhance local representations through neighborhood reconstruction and adaptive weighting. The augmented features were then integrated into a graph convolutional survival model optimized with the Cox partial likelihood. Results: PANA-Surv was evaluated on 10 cancer cohorts from The Cancer Genome Atlas (TCGA). The proposed method achieved the highest mean concordance index (C-index) among all compared models and significantly outperformed Cox-EN, DeepSurv, GraphSurv, and LAGProg (all p < 0.01). Ablation analyses showed that both neighborhood reconstruction and adaptive weighting contributed to the observed performance gains, and KEGG-guided graph construction was more effective than alternative graph construction strategies. In a breast cancer (BRCA) case study, PANA-Surv identified 18 prognostic genes, including 12 genes supported by previous studies and 6 potentially novel candidates. Conclusions: These findings indicate that the integration of pathway prior knowledge with adaptive local feature enhancement can improve multi-omics survival modeling and support the identification of biologically relevant prognostic signals associated with cancer outcomes. Full article

Standard Bayesian autoregressive changepoint models can become unstable near sample boundaries. As a candidate changepoint approaches either edge of the series, the local residual degrees of freedom shrink, producing a Gamma-function singularity in the marginal likelihood that can strongly bias the posterior toward spurious edge detections. To address this issue, we introduce a regularization framework driven by local degrees of freedom. By incorporating a centripetal prior of the form $\pi \left(k\right)\propto {\left({\nu }_1{\nu }_2\right)}^{\lambda }$—where ${\nu }_1=k-2p-1$ and ${\nu }_2=n-k-p-1$—the proposed method is designed to counteract this boundary effect. Theoretical analysis shows that a regularization intensity of $\lambda \ge 1$ is sufficient to offset this boundary effect asymptotically. Simulation results confirm that this approach substantially mitigates the U-shaped error profile typical of unregularized estimators, yielding a more favorable accuracy–robustness trade-off relative to the standard frequentist baselines considered in our study. Finally, empirical applications to several 2022 natural gas benchmarks, including TTF, SHPGX LNG, JKM, NBP, and NYMEX Henry Hub, demonstrate the framework’s ability to distinguish persistent structural transitions from transient market turbulence. These results suggest that degree-of-freedom-based centripetal prior regularization can improve the stability of Bayesian changepoint inference in nonstationary time series. Full article

Background/Objectives: Sleep disturbances are highly prevalent in young individuals with eating disorders (EDs) and are associated with increased psychopathology and poorer clinical outcomes. However, sleep alterations in ED populations are heterogeneous and may reflect distinct underlying clinical profiles. The study aimed to identify sleep quality profiles and examine their clinical correlates in youth with EDs. Methods: A total of 288 youth outpatients with EDs completed the Pittsburgh Sleep Quality Index (PSQI), along with measures of eating and general psychopathology. Latent Profile Analysis (LPA) was conducted using PSQI scores to identify distinct sleep profiles. Multinomial logistic regression models were performed to assess clinical variables of profile membership. Results: A four-profile solution was identified: (1) less impaired sleepers, (2) medication-using sleepers, (3) global poor sleepers, and (4) sleep-initiation-difficulty sleepers. Profiles differed significantly in ED severity, affective symptoms, emotion regulation difficulties, and sleep-related eating behaviors. Profiles characterized by greater sleep impairment exhibited higher levels of binge eating, night eating, and psychological distress. Multinomial logistic regression analyses indicated that night eating was the largest contributor to latent profile membership across all comparisons, significantly increasing the likelihood of belonging to more impaired sleep profiles. Conclusions: Sleep in individuals with EDs is characterized by distinct and clinically meaningful profiles rather than a uniform pattern of impairment. These findings support the clinical utility of person-centered approaches to better characterize sleep disturbances in ED populations. Full article

Background: This study aims to assess how patient comorbidities and risk factors influence treatment outcomes of periprosthetic joint infection (PJI). The role of timing for DAIR intervention, administration of antibiotics, and the microbiological profile in relation to infection recurrence were investigated. Methods: This retrospective study included 58 patients, 26 after total hip arthroplasty (THA) and 32 after total knee arthroplasty (TKA), who underwent surgery for early PJI managed with the complete DAIR protocol at a single academic orthopedic center (Professor Adam Gruca Orthopedic and Trauma Teaching Hospital) between January 2014 and January 2021. A minimum follow-up period after DAIR was five years. Results: In the overall cohort, therapeutic success was achieved in 41 of 58 patients (71%). Treatment of early PJI after THA was successful in 21 of 26 patients (81%), while after TKA, 20 of 32 patients (63%) achieved a favorable outcome. An increase in the number of comorbidities associated with infection risk was correlated with a lower likelihood of successful treatment using the DAIR protocol. Our analysis also demonstrated that the timing from total joint arthroplasty (TJA) to surgical intervention, the administration of antimicrobial therapy, and positive culture results influenced the success rate. Conclusions: The effectiveness of the DAIR protocol in managing early PJI is influenced by multiple factors. This study suggests that crucial determinants include prompt and accurate diagnosis, identification of patient-specific risk factors, the causative pathogen and its antibiotic administration, as well as the timing of intervention. Full article

Background/Objectives: Foreign-body aspiration (FBA) is a common and largely preventable pediatric emergency, yet current safety standards and risk assessments rely predominantly on object size and on anecdotal descriptions and bronchoscopy findings. We propose a clinically oriented proof-of-concept workflow that combines high-resolution three-dimensional (3D) scanning and calibrated two-dimensional (2D) imaging of retrieved objects with radiomic shape descriptors and large language model (LLM) reasoning to support aspiration risk assessment and guide prevention. Methods: Objects were obtained from the Susy Safe registry and historical series from the University Clinical Centre Tuzla. Each object was digitized with 3D scanning and photographed with a ruler. Morphometric descriptors—including volume, surface area, sphericity, elongation, flatness, curvature and convexity—were computed from stereolithography (STL) meshes; silhouette area, perimeter and Feret diameters were extracted from 2D photographs. Normative airway dimensions from radiographic and computed tomography (CT) studies provided anatomical context. A sharp, irregular metallic object recovered from a child’s laryngo-tracheal tract served as an illustrative case. Results: The object’s major axis approximated the anteroposterior glottic diameter, suggesting potential traversal when longitudinally oriented, whereas its irregular shape increased the likelihood of mucosal laceration and lodging. LLM-based synthesis provided a structured narrative interpretation consistent with a high-risk profile and highlighted preventive implications. Conclusions: Combining 2D/3D morphometry with LLM reasoning provides objective assessment of FBA hazards and may support safer product design, injury-prevention policies, and caregiver education. Full article

Background/Objectives: Pediatric dyslipidemia is a major modifiable risk factor for future cardiovascular disease, and lifestyle modification is recommended as first-line therapy. However, real-world longitudinal evidence on the effectiveness of repeated lifestyle education delivered during routine clinical practice remains limited. In this study, we assessed longitudinal metabolic changes following repeated lifestyle education and explored developmental and early-life factors associated with treatment responsiveness. Methods: In this retrospective longitudinal cohort study, we included 437 children and adolescents newly diagnosed with dyslipidemia at a tertiary hospital between 2019 and 2024. Participants received repeated lifestyle education during routine outpatient visits. Anthropometric and laboratory parameters were assessed over time. Linear mixed models were used to evaluate longitudinal changes, and multivariable logistic regression analyses were performed to identify predictors of lipid improvement. Results: Repeated lifestyle education was associated with gradual improvements in BMI SDS, total cholesterol, and non-HDL cholesterol over time. Linear mixed model analyses demonstrated significant time effects for total cholesterol and non-HDL cholesterol, while HDL cholesterol remained relatively stable. Thyroid-stimulating hormone (TSH) also demonstrated a significant time-dependent reduction during follow-up. Multivariable logistic regression analysis revealed that pubertal stage was associated with a lower likelihood of improvement in LDL and non-HDL cholesterol, whereas large-for-gestational-age birth was associated with a higher likelihood of HDL improvement. Conclusions: Repeated lifestyle education delivered during routine clinical practice was associated with meaningful improvements in lipid profiles in children with dyslipidemia. Developmental stage and early-life characteristics may influence treatment responsiveness, highlighting the importance of individualized and developmentally informed management strategies. Full article

Introduction: The role of Collis gastroplasty has traditionally been reserved for patients with a shortened esophagus due to chronic gastroesophageal reflux disease (GERD). However, its necessity has been questioned, leading to a decline in popularity. This systematic review and meta-analysis aimed to evaluate the efficacy of hiatal hernia repair with fundoplication, with versus without Collis gastroplasty. Methods: A systematic search of Ovid MEDLINE, Ovid Embase, Scopus, Web of Science Core Collection, and the Cochrane Library (via Wiley) was performed in May 2025. Studies were included if they compared outcomes or the safety profile of Collis gastroplasty versus no Collis gastroplasty during fundoplication for hiatal hernia repair. Meta-analyses were conducted using a random-effects model and restricted maximum likelihood. Results: Of 664 unique results, 17 studies comprising 4048 patients were included. There was a female predominance (65.4%), with a weighted mean age of 58.9 ± 14.0 years and follow-up of 43.5 ± 43.1 months. Patients who underwent Collis gastroplasty represented 35.8% of the cohort. Nissen fundoplication was the most common procedure in both the Collis (91.9%) and non-Collis (84.5%) groups. Most studies had selection bias, in which only patients who did not have sufficient intraoperative intra-abdominal esophageal length underwent Collis gastroplasty. Recurrence rates were similar (13.5% vs. 13.2%). Collis gastroplasty was not associated with a reduction in hiatal hernia recurrence (OR 0.53, 95% CI 0.23–1.22). Symptom outcomes, including regurgitation (OR 0.53, 95% CI 0.05–5.39), reflux (OR 0.81, 95% CI 0.03–22.12), dysphagia (OR 1.12, 95% CI 0.62–2.04), and use of antireflux medication on follow-up (OR 1.15, 95% CI 0.62–2.15), were not significantly different. However, Collis gastroplasty was associated with a higher risk of complications, including overall complications (OR 2.63, 95% CI 1.55–4.46), leak (OR 3.35, 95% CI 1.11–10.05), and surgical site infection (OR 8.28, 95% CI 1.16–59.10). There were no significant differences in abscess formation (OR 5.97, 95% CI 0.77–46.49), length of stay (mean difference 0.36 days, 95% CI −0.30 to 1.01), readmission (OR 1.13, 95% CI 0.36–3.60), reoperation (OR 1.24, 95% CI 0.64–2.41), or mortality (OR 1.08, 95% CI 0.45–2.57). Conclusions: Collis gastroplasty was not associated with a decreased risk of hiatal hernia recurrence or improvement in other efficacy measures, but this is in the context of a strong component of selection bias. In this context, there may be a role for Collis gastroplasty in difficult cases if the rate of recurrence does not differ from those with sufficient length, but this must be balanced against a significantly increased risk of complications. Full article