Nevin Manimala

Int J Gynaecol Obstet. 2026 Feb 4. doi: 10.1002/ijgo.70849. Online ahead of print.

ABSTRACT

OBJECTIVE: This study compares medium-term outcomes of retropubic tension-free vaginal tape (TVT) and transobturator tape (TOT) for stress urinary incontinence (SUI), focusing on patient satisfaction and overactive bladder (OAB) symptoms.

METHODS: This prospective, single-surgeon cohort study included women with SUI who underwent TVT or TOT at a single center (July 2021-October 2022). Follow-up was conducted at 26-41 months through chart review and patient interviews. Satisfaction was rated on a 0-100% global scale (≥75% = satisfied). Outcomes and complications followed International Continence Society criteria. Continuous variables were compared with the Mann-Whitney U-test and categorical variables with Fisher’s exact test. Sensitivity analysis addressed the effects of loss to follow-up. Statistical significance was set at P < 0.05.

RESULTS: Fifty-three women (25 TVT, 28 TOT) completed follow-up. Satisfaction (≥75%) was reported by 88.0% of TVT and 89.3% of TOT patients (P ≈ 1.00). Sensitivity analyses assuming all lost patients were satisfied or unsatisfied did not alter statistical significance (P ≈ 1.00 and P = 0.54, respectively). Among women with pre-existing overactive bladder, improvement in symptoms occurred in 78.3% (18/23) of TVT and 61.1% (11/18) of TOT patients (P = 0.47). One bladder perforation occurred in the TVT group; other complications were infrequent and similar between groups.

CONCLUSIONS: Both TVT and TOT provided high satisfaction and improvement in OAB symptoms at 26-41 months, with low complication rates. These findings suggest that both procedures remain safe and effective in the medium term, reinforcing their established role in the surgical management of SUI.

PMID:41635999 | DOI:10.1002/ijgo.70849

J Endourol. 2026 Feb 4:8927790261416442. doi: 10.1177/08927790261416442. Online ahead of print.

ABSTRACT

PURPOSE: To investigate the efficacy and safety of single-port (SP) robotic nephroureterectomy (NUx) with bladder cuff excision.

MATERIALS AND METHODS: From September 2021 to August 2024, we reviewed all patients diagnosed with urothelial carcinoma who underwent robot-assisted laparoscopic NUx at our institution since the introduction of the SP robot.

RESULTS: A total of 105 patients were included in the study, of whom 52 underwent surgical procedure using the multi-port (MP) approach, whereas 53 underwent surgery using the SP approach. No statistically significant differences were found in patient characteristics, such as gender, body mass index, or tumor size. In terms of surgical outcomes, no statistically significant differences were found in key metrics, such as console time and estimated blood loss. However, a statistically significant difference was observed in total operative time, with an average difference of 45 minutes (222.25 ± 69.38 minutes in MP, 169.98 ± 49.63 minutes in SP, p = 0.000). The estimated blood loss was lower with the SP robot (144.91 mL ± 108.25 in MP, 96.68 ± 72.95 mL in SP, p = 0.004). During the one-year follow-up, no statistically significant differences in renal function loss or T stage were observed.

CONCLUSION: NUx with bladder cuffing using the SP approach demonstrated feasibility compared with surgery performed using the MP approach. Notably, the ease of access during cuffing contributed significantly to reducing the total operative time.

PMID:41635986 | DOI:10.1177/08927790261416442

Spec Care Dentist. 2026 Jan-Feb;46(1):e70144. doi: 10.1111/scd.70144.

ABSTRACT

OBJECTIVES: This study aimed to examine the association between oral health factors and Mild Cognitive Impairment (MCI), evaluating their independent effects after adjustments for sociodemographic, medical, and behavioral confounders.

METHODS: A cross-sectional analytical study was conducted among 248 older adults aged 60 years and above. Cognitive status was assessed using the Montreal Cognitive Assessment-Thai version (MoCA-T). Demographic, medical, and behavioral data were collected through structure interviews. Oral health assessments included active dental caries, periodontal disease, number of natural teeth, number of posterior occluding pairs, and masticatory performance, all measured through clinical examination. A multivariate logistic regression analysis was performed using the enter method, with statistical significance set at p < 0.05.

RESULTS: The mean age of participants was 68.7 years, and 73% were female. Of the 248 participants, 73 (29.4%) were identified as having MCI. After adjusting for age, marital status, education, occupation, income, hypertension, functional, and nutritional status, only periodontal disease remained significantly associated with MCI (adjusted OR = 2.01, 95% CI: 1.05-3.84, p = 0.035).

CONCLUSION: Among the oral health factors examined, periodontal disease emerged as the only factor independently associated with MCI after adjustment for demographic, medical, and behavioral confounders.

PMID:41635984 | DOI:10.1111/scd.70144

Chemistry. 2026 Feb 4:e03299. doi: 10.1002/chem.202503299. Online ahead of print.

ABSTRACT

Machine learning (ML) models are increasingly used in quantum chemistry, but their reliability hinges on uncertainty quantification (UQ). In this study, we compare two prominent UQ paradigms-deep evidential regression (DER) and deep ensembles-on the QM9 and WS22 datasets, with a specific emphasis on the role of post hoc calibration. Raw uncertainties from both methods were systematically miscalibrated: DER produced uncertainty estimates where data noise and model uncertainty were not cleanly separated, while ensembles produced sharper yet underconfident estimates. Applying calibration techniques such as isotonic regression (ISR), standard scaling, and GP-Normal corrected these deficiencies, aligning predicted variances with observed errors. On QM9, calibration enabled DER to filter high-confidence predictions more effectively than ensembles. On WS22, calibrated ensembles not only improved statistical reliability but also delivered substantial computational savings in active learning, reducing redundant ab initio evaluations by more than 20%. These results demonstrate that post hoc calibration is essential to transform uncertainty estimates from descriptive metrics into actionable signals, ensuring both trustworthy predictions and resource-efficient molecular modeling.

PMID:41635978 | DOI:10.1002/chem.202503299

Surg Laparosc Endosc Percutan Tech. 2026 Feb 1;36(1):e1423. doi: 10.1097/SLE.0000000000001423.

ABSTRACT

BACKGROUND: Postoperative intra-abdominal abscess (IAA) is the most feared complication after laparoscopic appendectomy (LA). The management of IAA measuring 2 to 4 cm remains controversial. We aimed to compare the effectiveness of antibiotic treatment versus percutaneous drainage for the treatment of IAA measuring 2 to 4 cm following LA.

METHODS: A consecutive series of patients with post-appendectomy IAA measuring 2 to 4 cm from January 2006 to April 2024 was included for analysis. The patient cohort was divided into 2 groups according to the treatment modality: antibiotic therapy alone (ATB) versus computed tomography-guided percutaneous drainage (PERC). The primary outcome was to compare the success rate between groups. Secondary endpoints included overall and major complications, length of stay (LOS), readmissions, and mortality.

RESULTS: During the study period, 2700 LA were performed, and 123 (4.5%) patients developed an IAA. Of these, 47 (38%) measured 2 to 4 cm: 25 (53%) received antibiotics only (ATB), and 22 (47%) underwent percutaneous drainage (PERC). The success rates were comparable between groups (ATB: 92% vs. PERC: 95.4%, P=0.6). Patients who failed conservative management in both groups underwent laparoscopic lavage without further complications. No readmissions, morbidity or mortality were observed. The mean LOS was longer in the PERC group (ATB: 2.0 vs. PERC: 3.5 d, P=0.03).

CONCLUSIONS: Antibiotic therapy and percutaneous drainage are both highly effective for treating IAA measuring 2 to 4 cm following LA. Given the less invasive nature of antibiotic therapy with shorter length of stay, it should be considered the initial treatment of choice.

PMID:41635964 | DOI:10.1097/SLE.0000000000001423

J Neuroimaging. 2026 Jan-Feb;36(1):e70127. doi: 10.1111/jon.70127.

ABSTRACT

BACKGROUND AND PURPOSE: Grading meningioma guides treatment choices from follow-up to surgical resection with adjuvant radiation. Radiomics may offer a non-invasive alternative to biopsies. We assessed radiomic features (RFs) for distinguishing Grade 1 and Grade 2 meningiomas on preoperative multiparametric MRI.

METHODS: Presurgical T1-weighted (T1), T2-weighted (T2), T2 gradient echo-weighted (T2GRE), fluid-attenuated inversion recovery (FLAIR), apparent diffusion coefficient (ADC), and T1-weighted contrast-enhanced (T1CE). MRI sequences of histopathologically diagnosed meningiomas were collected retrospectively. Each volume had 75 RFs extracted from semimanually segmented tumors using MintLesion Research (Version 3.10). The Lasso method selected variables from imputed data, and 10-fold cross-validation determined the optimal regularization parameter. For Lasso-retained variables, multivariate effects were estimated.

RESULTS: Out of 150 patients (67.3% women), 110 (73.3%) had Grade 1 meningiomas, and 40 (26.7%) Grade 2. The strongest metrics to distinguish meningiomas Grade 1 versus Grade 2 were intensity histogram coefficient of variation on T1CE (odds ratio [OR] 0.47, 95% confidence interval [CI] 0.23-0.88; p = 0.028), maximum histogram gradient on T1 (OR 2.11, 95% CI 1.18-4.82; p = 0.043), and intensity histogram quartile coefficient of dispersion on FLAIR (OR 0.53, 95% CI 0.31-0.89; p = 0.021). The combined RFs achieved an area under the curve of 0.814 (95% CI, 0.732-0.896) for grading differentiation. Texture features and metrics extracted from T2, T2GRE, and ADC sequences did not discriminate meningioma grading.

CONCLUSIONS: Histogram-based first-order RFs from T1, FLAIR, and T1CE may predict meningioma grades preoperatively. Larger, multicenter studies are needed to confirm these findings, providing insights for clinical decision-making and personalized treatment.

PMID:41635960 | DOI:10.1111/jon.70127

Res Synth Methods. 2026 Mar;17(2):293-313. doi: 10.1017/rsm.2025.10050. Epub 2025 Nov 13.

ABSTRACT

Bayesian hierarchical models offer a principled framework for adjusting for study-level bias in meta-analysis, but their complexity and sensitivity to prior specifications necessitate a systematic framework for robust application. This study demonstrates the application of a Bayesian workflow to this challenge, comparing a standard random-effects model to a bias-adjustment model across a real-world dataset and a targeted simulation study. The workflow revealed a high sensitivity of results to the prior on bias probability, showing that while the simpler random-effects model had superior predictive accuracy as measured by the widely applicable information criterion, the bias-adjustment model successfully propagated uncertainty by producing wider, more conservative credible intervals. The simulation confirmed the model’s ability to recover true parameters when priors were well-specified. These results establish the Bayesian workflow as a principled framework for diagnosing model sensitivities and ensuring the transparent application of complex bias-adjustment models in evidence synthesis.

PMID:41635950 | DOI:10.1017/rsm.2025.10050

Res Synth Methods. 2026 Mar;17(2):314-331. doi: 10.1017/rsm.2025.10049. Epub 2025 Nov 13.

ABSTRACT

Ranking multiple interventions is a crucial task in network meta-analysis (NMA) to guide clinical and policy decisions. However, conventional ranking methods often oversimplify treatment distinctions, potentially yielding misleading conclusions due to inherent uncertainty in relative intervention effects. To address these limitations, we propose a novel Bayesian rank-clustering estimation approach, termed rank-clustering estimation (RaCE), specifically developed for NMA. Rather than identifying a single “best” intervention, RaCE enables the probabilistic clustering of interventions with similar effectiveness, offering a more nuanced and parsimonious interpretation. By decoupling the clustering procedure from the NMA modeling process, RaCE is a flexible and broadly applicable approach that can accommodate different types of outcomes (binary, continuous, and survival), modeling approaches (arm-based and contrast-based), and estimation frameworks (frequentist or Bayesian). Simulation studies demonstrate that RaCE effectively captures rank-clusters even under conditions of substantial uncertainty and overlapping intervention effects, providing more reasonable result interpretation than traditional single-ranking methods. We illustrate the practical utility of RaCE through an NMA application to frontline immunochemotherapies for follicular lymphoma, revealing clinically relevant clusters among treatments previously assumed to have distinct ranks. Overall, RaCE provides a valuable tool for researchers to enhance rank estimation and interpretability, facilitating evidence-based decision-making in complex intervention landscapes.

PMID:41635948 | DOI:10.1017/rsm.2025.10049

Res Synth Methods. 2026 Mar;17(2):378-388. doi: 10.1017/rsm.2025.10052. Epub 2025 Nov 24.

ABSTRACT

Accurate assessment of adverse event (AE) incidence is critical in clinical research for drug safety. While meta-analysis serves as an essential tool to comprehensively synthesize the evidence across multiple studies, incomplete AE reporting in clinical trials remains a persistent challenge. In particular, AEs occurring below study-specific reporting thresholds are often omitted from publications, leading to left-censored data. Failure to account for these censored AE counts can result in biased AE incidence estimates. We present an R Shiny application that implements a Bayesian meta-analysis model specifically designed to incorporate censored AE data into the estimation process. This interactive tool provides a user-friendly interface for researchers to conduct AE meta-analyses and estimate the AE incidence probability using an unbiased approach. It also enables direct comparisons between models that either incorporate or ignore censoring, highlighting the biases introduced by conventional approaches. This tutorial demonstrates the Shiny application’s functionality through an illustrative example on meta-analysis of PD-1/PD-L1 inhibitor safety and highlights the importance of this tool in improving AE risk assessment. Ultimately, the new Shiny app facilitates more accurate and transparent drug safety evaluations. The Shiny-MAGEC app is available at: https://zihanzhou98.shinyapps.io/Shiny-MAGEC/.

PMID:41635945 | DOI:10.1017/rsm.2025.10052

Clin Kidney J. 2025 Dec 8;19(2):sfaf370. doi: 10.1093/ckj/sfaf370. eCollection 2026 Feb.

ABSTRACT

BACKGROUND: Despite the high suicide rates among patients with end-stage kidney disease (ESKD), there is no suicide prediction model specifically designed for this vulnerable population. Herein, we aimed to develop and validate a novel suicide risk score for ESKD patients.

METHODS: We analyzed data from the National Health Insurance Service (NHIS) of South Korea, including 251 819 patients aged above 18 years diagnosed with ESKD between 2007 and 2022 in South Korea. The mean follow-up duration was 6.6 years. The cohort was randomly divided into derivation (70%) and validation (30%) sets. Using multivariate Cox proportional hazard regression, key variables were incorporated to develop the suicide risk score, which was converted into a 48-point scoring system, which is composed of easily identifiable clinical parameters.

RESULTS: Among 176 273 patients in the derivation cohort, 1126 (0.64%) patients committed suicide. The suicide risk score demonstrated moderate discrimination in both the derivation (C-statistic, 0.694) and validation (C-statistic, 0.709) cohorts, with good calibration. In the validation cohort, patients scoring below 16, 17-32 and 33-48 had predicted 10-year suicide risk of 0.2%, 1.2% and 7.7%, respectively, while the observed 10-year risk were 0.3%, 0.8% and 3.9%. These findings highlight the model’s ability to effectively stratify risk using routinely available clinical data.

CONCLUSIONS: The suicide risk score is a significant advancement in suicide risk prediction for ESKD patients. It is based on simple, routinely collected clinical indicators and provides an actionable tool for risk stratification and early intervention in daily practice.

PMID:41635920 | PMC:PMC12863073 | DOI:10.1093/ckj/sfaf370