Nevin Manimala

Int J Gynaecol Obstet. 2026 May 23. doi: 10.1002/ijgo.71076. Online ahead of print.

ABSTRACT

OBJECTIVE: The aim of the present study was to assess changes in cesarean section (CS) rates over 3 years at a public tertiary care hospital using the Robson Ten Group Classification System (RTGCS). The specific objectives were to compare population distribution across Robson groups, evaluate overall, group-specific, absolute and relative CS rates and assess data quality and completeness.

METHODS: This analytical cross-sectional study was conducted at a public tertiary care hospital in Rawalpindi, Pakistan and spanned over two phases. Phase 1: June 2019 to November 2019 and phase 2: August 2022 to January 2023. For analysis, RTGCS was applied. Robson’s report tables (RRTs) of two phases were generated to calculate and compare CS rates. Statistical analysis included the calculation of P values (considering <0.05 as significant) by chi-square and Fisher exact tests, odds ratios (ORs), relative risks (RRs) and 95% confidence intervals (CIs), using established online tools.

RESULTS: The total number of women included in study during phase 1 were 5437, and in phase 2 were 3762. Overall CS rate increased from 31.06% (phase 1) to 40.09% (phase 2) (P < 0.001). In both phases, Robson group 3 was the largest (33% and 25.2% in phases 1 and 2, respectively) and group 9 was the smallest (<1%). The highest absolute contribution towards CS rate was made by group 5, followed by groups 10 and 2 in both phases. Combined contribution of groups 5, 10, and 2 was 70.5% in phase 1 and 64.9% in phase 2. Group 9 had the highest group-specific CS rate in both phases (93.7% and 100%). In phase 1, it was followed by groups 5 (80.4%), 6 (78.2%) and 7 (72%), while in phase 2 by groups 6 (89.6%), 5 (85.2%), and 8 (72.2%). In CS rate, a statistically significant increase was noted in Robson groups 1, 3, 5, 6, 8, and 10, while a decline was observed in group 4 only.

CONCLUSION: The overall CS rate significantly increased over time. This rise was mainly due to a shift in the obstetric population in high-risk groups; 6, 8, 9, 10, and group 5. Conversely, the reduced CS rates in groups 2 and 4 suggest improved induction practices. The data quality was satisfactory.

PMID:42175756 | DOI:10.1002/ijgo.71076

Risk Anal. 2026 Jun;46(6):e70267. doi: 10.1111/risa.70267.

ABSTRACT

High temperatures are increasingly associated with adverse mental health outcomes, yet the influence of structural modeling assumptions on these estimates remains underexplored. This study examined the short-term association between high temperatures and mental health-related-hospitalizations in 21 major Italian cities from 2005 to 2023, using national hospital discharge data. Exposure-lag-response relationships were modeled through a Distributed Lag Nonlinear Model (DLNM) framework estimated within a Generalized Additive Model (GAM). Analyses focused on June-September and included hospitalizations with a primary diagnosis of mental disorders, considering the code 295-316 of the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM). A Global Sensitivity Analysis (GSA) assessed how structural decisions, such as the specification of temperature and lag splines, and the inclusion of September, affect risk estimates. A total of 210,310 hospitalizations were recorded. The cumulative exposure-response curve showed a marked nonlinear increase in risk, reaching relative risk values close to two at temperatures exceeding 40 ${}^{\circ }C$ . The GSA revealed that the number and placement of knots in the temperature dimension were the dominant contributors to output variability, with total-order sensitivity indices approaching one across most of the temperature range. Variations in the lag structure contributed minimally, while including or excluding September influenced model fit but only modestly affected risk estimates. Uncertainty quantified through GSA was substantially larger than that quantified by standard confidence intervals, indicating that structural assumptions meaningfully shape inference. Within the DLNM-GAM framework, high summer temperatures were consistently associated with increased psychiatric hospitalizations. Incorporating GSA clarified which modeling choices influence estimates, improving transparency and robustness in evaluating heat-related mental health impacts.

PMID:42175751 | DOI:10.1111/risa.70267

Eur J Prev Cardiol. 2026 May 23:zwag267. doi: 10.1093/eurjpc/zwag267. Online ahead of print.

ABSTRACT

Randomized controlled trials (RCTs) remain the gold standard for causal inference in cardiovascular prevention but are often limited by cost, feasibility, and restricted generalizability. The rapid expansion of real-world data (RWD) offers new opportunities to address clinically relevant questions beyond the scope of RCTs, yet observational analyses remain highly susceptible to bias, particularly when study design is not aligned with the underlying causal question. Target trial emulation (TTE) is an increasingly adopted framework that improves the validity and interpretability of observational studies by explicitly specifying the protocol of the hypothetical randomized trial that would ideally answer the clinical question. This review provides a practical guide to TTE in cardiovascular prevention. We describe its conceptual foundations within the counterfactual framework, emphasizing the shift from a model-driven to a design-first approach, and outline the seven key components of the target trial protocol: eligibility criteria, treatment strategies, assignment procedures, time zero definition, outcomes, estimand, and statistical analysis plan. We clarify the role of analytical methods within TTE, including propensity score approaches, g-computation, and g-estimation, and provide guidance on selecting the appropriate method based on the estimand and treatment strategy. A step-by-step implementation framework is proposed, covering common pitfalls such as immortal time bias and prevalent user bias, the use of negative control analyses as diagnostic tools, and the handling of missing data. Illustrative examples from cardiovascular prevention demonstrate how TTE enhances causal interpretation across a range of clinical questions. TTE strengthens the credibility of real-world evidence by improving transparency, reducing avoidable design biases, and aligning analyses with clinically meaningful decisions. It does not eliminate residual confounding and should be viewed as complementary to, rather than a substitute for, randomized evidence.

PMID:42175748 | DOI:10.1093/eurjpc/zwag267

Chronobiol Int. 2026 May 23:1-9. doi: 10.1080/07420528.2026.2678271. Online ahead of print.

ABSTRACT

Excessive use of smartphones and tablets has become increasingly common among children, contributing to adverse physical and mental health outcomes. Evening chronotype and inadequate sleep habits have been identified as potential risk factors. However, despite growing concerns, there is limited evidence on the relationship between chronotype, sleep patterns, and digital device addiction in children. In this cross-sectional study, 213 Iranian schoolchildren aged 4-11 years were assessed using validated Persian versions of three standardized instruments: the Children’s Chronotype Questionnaire (CCTQ), the Children’s Sleep Habits Questionnaire (CSHQ), and the Smartphone and Tablet Addiction Questionnaire. This is the first study in Iran to employ the CCTQ to examine the association between chronotype and smartphone addiction in children. Data analysis included descriptive statistics, Spearman’s correlation, stepwise regression, and Kruskal-Wallis tests. Sleep problems and smartphone/tablet addiction were significantly more prevalent in children with an evening chronotype, followed by those with a neutral chronotype, and were lowest in those with a morning chronotype. In addition, the M/E and CSHQ scores were positively associated with smartphone/tablet addiction scores, whereas later sleep onset time on scheduled days showed a negative association. Specifically, each one-unit increase in the M/E score was linked to a 0.75-point increase in the average addiction score (β = 0.75, SE = 0.17, p < 0.01), and each one-unit increase in the CSHQ score corresponded to a 0.35-point increase (β = 0.35, SE = 0.13, p < 0.05). Conversely, each one-unit delay in sleep onset time on scheduled days was associated with a 0.15-point decrease in the average addiction score (β = -0.15, SE = 0.09, p < 0.05). Children with an evening chronotype appear more susceptible to both poor sleep habits and smartphone/tablet addiction. These findings, in line with previous research on adolescents and adults, support the notion that circadian misalignment plays a pivotal role in technology-related behavioral risks. Early identification of chronotype and the implementation of strategies to promote healthier sleep schedules may be beneficial for fostering healthier sleep patterns and more balanced device use among children.

PMID:42175734 | DOI:10.1080/07420528.2026.2678271

Am Surg. 2026 May 23:31348261455090. doi: 10.1177/00031348261455090. Online ahead of print.

ABSTRACT

BackgroundPostoperative respiratory depression (PRD) is potentially preventable yet remains difficult to preemptively detect. We evaluated whether three post anesthesia care unit (PACU) events-oversedation, caffeine administration for impaired arousal, and naloxone administration-can serve as early markers of delayed PRD requiring naloxone administration on wards.MethodsWe retrospectively identified patients who underwent general anesthesia between 2018 and 2023 at a quaternary care academic medical center. From electronic medical records, we retrieved PACU naloxone and caffeine treatments, scores of sedation assessments using the Richmond Agitation-Sedation Scale (RASS), and ward naloxone administrations within 24 hours after PACU discharge.ResultsAmong 95 870 patients, 186 (0.19%, 95% CI 0.17-0.22) required naloxone for respiratory depression after PACU discharge. Ward naloxone administration was independently associated with naloxone (OR 9.11, 95% CI 4.69-17.71, P < 0.001) and caffeine (OR 2.00, 95% CI 1.21-3.32, P = 0.007) administrations, and with PACU RASS scores ≤ -3 (OR 2.16, 95% CI 1.56-2.99, P < 0.001).ConclusionsNaloxone administration in PACU was the strongest predictor of delayed PRD, followed by oversedation and PACU caffeine administration, indicating that information routinely collected during PACU recovery may offer insight into delayed respiratory risk before transition to hospital wards. In light of the overall low incidence of ward naloxone use, these findings support selective, risk-based vigilance for patients exhibiting these PACU events rather than broad adjustments to existing monitoring practices.

PMID:42175723 | DOI:10.1177/00031348261455090

J Magn Reson Imaging. 2026 May 23. doi: 10.1002/jmri.70350. Online ahead of print.

ABSTRACT

BACKGROUND: Motion can degrade image quality during Ultrashort Time-to-Echo (UTE) pulmonary MRI and is particularly prevalent in patients with lung disease. Comprehensive assessment of the impact of motion compensation techniques on image quality and clinical interpretation is needed.

PURPOSE/HYPOTHESIS: To compare the impact of retrospective motion compensation schemes on image quality and clinical interpretation of pulmonary UTE MRI in idiopathic pulmonary fibrosis (IPF).

STUDY TYPE: Prospective.

POPULATION: 21 (male = 18; mean age, 69.9 ± 8.1 years) participants with suspected IPF.

FIELD STRENGTH/SEQUENCE: 1.5 T/3 T, 3D center-out radial (gradient-echo) UTE sequence with 2× radial oversampling, while free-breathing.

ASSESSMENT: Images were reconstructed to 1.25 mm isotropic resolution using five retrospective schemes: no gating, hard-gating, soft-gating, motion-resolved (XD-GRASP), and an iterative approach (iMoCo). Signal-to-noise ratios (SNR) were estimated within the lung parenchyma, airways, aorta, muscles, and liver. Contrast-to-noise ratios (CNR) were estimated using the mean airway signal as reference. Image sharpness was estimated using the maximum derivative of a line profile across the diaphragm and a wavelet-based autofocus measure. Three radiologists evaluated image quality, motion artifacts on a 5-point Likert scale, and diagnostic classification of usual interstitial pneumonia (UIP).

STATISTICAL TESTS: The Kruskal-Wallis non-parametric test was used for qualitative reader scores and one-way ANOVA for the quantitative metrics, with p < 0.05 as the threshold for significance.

RESULTS: CNR was highest using the iMoCo reconstructions (lung parenchyma: 1.64 ± 1.41 vs. 0.88 ± 0.81 via XD-GRASP). Image sharpness was significantly improved using compressed sensing (CS)-based techniques (XD-GRASP and iMoCo), compared to the other methods, using both diaphragm profile (CS: 6.28 ± 3.70 vs. non-CS: 3.73 ± 2.06) and wavelet metrics (CS: 2.33 ± 0.42 vs. non-CS: 2.05 ± 0.35). CS methods also demonstrated greatest image quality based on reader scores.

CONCLUSION: Motion compensation using compressed sensing methods can improve image quality and clinical utility of UTE-MRI in the identification and diagnostic classification of typical parenchymal fibrotic patterns.

EVIDENCE LEVEL: Level 2-Prospective study, with a reference standard determined during the course of the study (CT imaging).

TECHNICAL EFFICACY: Stage 1.

PMID:42175722 | DOI:10.1002/jmri.70350

Emerg Microbes Infect. 2026 May 23:2678657. doi: 10.1080/22221751.2026.2678657. Online ahead of print.

ABSTRACT

AbstractPulmonary infections caused by nontuberculous mycobacteria (NTM), particularly Mycobacterium avium complex (MAC), are increasingly recognized as an important clinical entity, yet distinguishing active pulmonary disease from asymptomatic colonization remains challenging because current diagnosis relies on composite criteria. In this study, we aimed to identify blood transcriptomic signatures that discriminate MAC pulmonary disease (MAC-PD) from pulmonary colonization (MAC-PC) and to evaluate their potential as candidate biomarkers. MAC-positive patients from two medical centers in Taiwan were enrolled as training and external validation cohorts, and peripheral blood transcriptomes were profiled. Candidate genes were identified using least absolute shrinkage and selection operator regression and recursive feature elimination across multiple random seeds, followed by filtering based on statistical robustness and biological relevance. Machine-learning models were then trained and externally validated. Among 120 patients (training cohort: 46 MAC-PD and 28 MAC-PC; validation cohort: 25 MAC-PD and 21 MAC-PC), seven enriched gene ontology terms were prioritized. Three models demonstrated robust performance in the validation cohort, with areas under the receiver operating characteristic curve of 0.78, 0.78, and 0.75, and accuracies of 0.76, 0.74, and 0.74, respectively. These models shared a five-gene core signature consisting of IGKV1D-39, IGKV6-21, OVCH1, PLAU, and DMD, highlighting convergent biological signals related to immune responses and tissue remodeling. Overall, blood transcriptomic profiling shows promise in differentiating MAC-PD from MAC-PC in our cohorts, and the identified five-gene core signature represents a biologically coherent, minimally invasive candidate biomarker panel warranting further prospective validation.

PMID:42175713 | DOI:10.1080/22221751.2026.2678657

Biomed Res Int. 2026;2026(1):e8893135. doi: 10.1155/bmri/8893135.

ABSTRACT

BACKGROUND: Medication safety is an important public health challenge, especially in pediatrics. Medication errors (MEs) are often underreported in pediatrics and can lead to adverse outcomes such as frequent readmissions, increased total healthcare costs, prolonged hospitalization, and related morbidity and mortality. Thus, this study is aimed at assessing the magnitude and determinants of MEs among pediatric hospitalized patients at comprehensive specialized hospitals in Northwest Ethiopia.

METHODS: A multicenter prospective observational study involving pediatric hospitalized patients was conducted over 4 months, utilizing systematic random sampling for participant selection. Three clinical pharmacists, after a day of training, collected data under the supervision of an MSc health professional, with support from pediatricians in each hospital for reviewing MEs and adjusting treatment plans. Pediatric patients were followed prospectively during their hospital stay from admission to discharge. Data collection occurred via the Kobo Toolbox platform and was analyzed with STATA Version 17.0. Both bivariate and multivariable logistic regression analyses identified factors related to MEs, with statistical significance set at a p value < 0.05.

RESULTS: Among 358 pediatric hospitalized patients, 53.63% experienced at least one ME, totaling 254 identified errors. The prescribing stage accounted for the highest percentage of errors (40.16%), followed by the administration stage (32.68%). The predominant types of MEs were dose errors (30.31%), frequency errors (14.96%), and omission errors (14.17%). Multivariable logistic regression analysis revealed that polypharmacy (≥ 5 medications) (AOR = 2.005, 95% CI: 1.269-3.168), male sex (AOR = 1.707, 95% CI: 1.097-2.656), and prolonged hospital stay (AOR = 1.673, 95% CI: 1.076-2.602) were significantly associated with the occurrence of MEs.

CONCLUSION: This study found that MEs were prevalent in pediatric hospitalized patients. Polypharmacy, male patients, and the length of hospital stay were independent predictors of MEs. To reduce MEs, computer-based prescribing practice and clinical pharmacy services should be routine practices in the study settings.

PMID:42175692 | DOI:10.1155/bmri/8893135

J Am Acad Orthop Surg Glob Res Rev. 2026 May 19;10(5). doi: 10.5435/JAAOSGlobal-D-25-00449. eCollection 2026 May 1.

ABSTRACT

OBJECTIVE: Fiberglass long leg casting is often used to treat specific lower extremity fracture patterns in children. However, cylindrical casting limits swelling, increasing risk of compartment syndrome. To account for edema, casts are frequently univalved, but it remains unclear whether univalve location affects skin surface pressures (SSPs) in long leg casts. We hypothesized that a lateral univalve would decrease anterior SSP, whereas univalve location would not affect posterior SSP in long leg casts.

METHODS: A 100-mL saline bag attached to a pressure transducer was placed along the anterior or posterior compartment of a volunteer underneath 20 and 26 long leg casts, respectively. The casts were randomly assigned to receive either lateral or medial univalve. The bag was insufflated with water to 100 mm Hg, and change in SSP was recorded with univalve (stage I), univalve with 3-mm spacer (stage II), univalve with 6-mm spacer (stage III), and bivalve (stage IV). Statistical analysis was done to detect an SSP difference of 10 mm Hg.

RESULTS: In the anterior and posterior compartments, no notable differences were found in SSP change within any stage between lateral and medial univalve. Comparing stage I and stage IV, a notable SSP change was found across all anterior and posterior compartment groups (P < 0.001, 95% confidence).

CONCLUSION: No notable difference was found in anterior or posterior SSP in long leg casts with either medial or lateral univalve. Our data support a “dealer’s choice” that the practitioner may select either medial or lateral univalve to reduce anterior and posterior SSP.

PMID:42175675 | DOI:10.5435/JAAOSGlobal-D-25-00449

J Inherit Metab Dis. 2026 May;49(3):e70202. doi: 10.1002/jimd.70202.

ABSTRACT

Neuronal ceroid lipofuscinosis type 2 (CLN2) disease, a lysosomal storage disorder, causes early childhood psychomotor regression, vision loss, seizures, and rapid progressive gray matter loss. However, the link between neurodegenerative processes induced by lysosomal pathophysiology and the clinical phenotype remains unclear. This study investigated the longitudinal association of gray matter atrophy on MRI with in-depth clinical phenotyping in 27 patients receiving intraventricular enzyme therapy (n_timepoints = 170; biannual clinical assessments and MRIs). Longitudinal changes in cortical thickness and subcortical volumes were modeled via linear mixed effects regression. We used linear regression to correlate 24-week (Δ24) changes in clinical assessments with global cortical thickness and applied multivariate data-driven statistics to model how specific brain regions are associated with clinical domains. Our analysis revealed a significant reduction in the mean cortical thickness over time (β = -0.002, p = 0.021), corresponding to an annual loss of 4.2%, compared to natural history controls with 12.5%, respectively. Regional analysis revealed a widespread pattern of cortical and subcortical gray matter atrophy. Global cortical thickness reductions over 24 weeks (Δ24) were significantly associated with changes in the Hamburg motor and language scale Δ24, Weill Cornell scale Δ24, and Movement Disorder Inventory Δ24. Multivariate statistics identified a significant latent dimension relating regional morphometric abnormalities to worse clinical outcomes, accounting for 82% of the shared variance. Leveraging connectome data, we demonstrated that atrophy was linked to brain network architecture. Given their strong associations with clinical outcomes, MRI-based brain morphometric measures are promising CLN2 disease biomarkers to aid diagnosis, monitor disease progression, and guide therapy.

PMID:42175674 | DOI:10.1002/jimd.70202