Search Results (793)

Background/Objectives: Family-Centered Care (FCC) is an established standard in pediatric rehabilitation, but it has been studied almost entirely inside health services; whether its principles survive the move into community settings is largely unknown. This paper describes how caregivers perceive the family-centeredness of a community-based adapted sport program and characterizes child quality of life and caregiver well-being. Methods: Cross-sectional study within the “Skill-In” program (University of Messina). Nineteen caregivers of children with neurodevelopmental disabilities completed the MPOC-20, the KIDSCREEN-52 proxy version and the CarerQoL, analyzed with Spearman correlations and bootstrap confidence intervals. Results: The overall MPOC-20 mean item score was 5.43/7 (SD 0.83), concealing a wide spread from Respectful and Supportive Care (6.25) to Providing General Information (3.78). “Not applicable” responses (14.2%) clustered in items presupposing a clinical provider. KIDSCREEN-52 raw sums were converted to Rasch-based T-values (norm: mean 50, SD 10). Eight of ten dimensions fell within half a standard deviation of the norm, including Social Acceptance (47.65); two did not, Social Support and Peers (34.58) and Autonomy (39.42), each over one standard deviation below. Caregivers reported favorable well-being (CarerQoL-VAS 7.32/10), though six of nineteen reported no support. Associations between care processes and family outcomes were weak. Conclusions: A sport organization with no clinical mandate delivered the relational core of family-centered care comparably to rehabilitation services, while falling short on structured information. The children’s quality-of-life profile was normal on most dimensions but selectively low on peer support and autonomy. Acceptance, on this evidence, is not connection: social participation must be designed for, not assumed. Full article

Short ordinal self-report scales are widely used to study children’s digital lives, yet their measurement properties can distort conclusions about nonlinear relationships. We introduced an integrated diagnostic workflow for such scales—covering range, structure, reliability, method variance and functional form—and applied it to the TIMSS 2023 Digital Self-Efficacy scale across all 63 Grade 4 and 47 Grade 8 education-system and benchmarking samples (source database N = 719,881 children; 630,461 with complete seven-item measurement data). The scale was endpoint-concentrated, markedly at Grade 8, losing 83% and 95% of its test information between the mean and two standard deviations above it; an exact marginal calculation from a testlet model gave 80% and 89%. The apparent multidimensionality was better represented as localized covariance among three similarly worded items than as a separable second dimension, and omega hierarchical of 0.79 and 0.83 supported using the total score. In a factorial simulation evaluating the population projection coefficient on the analysis scale, endpoint concentration raised rejection of no curvature from 5.4% to 16.7% with raw summed scores, while latent scoring returned it to 5.5% and raised power from 71% to 88%, whether the item parameters were known or, in a smaller supporting condition, estimated in the analysis sample. Applied to cybervictimization, the quadratic association was attenuated but did not reverse sign once covariates were matched, and its prediction interval included zero. The range and reliability diagnostics behaved similarly on a second scale from the same assessment; broader applicability of the full workflow is proposed on theoretical grounds rather than established here. Full article

Background/Objectives: Basal metabolic rate (BMR) is implicated in age-related phenotypes, and presbyopia represents a hallmark of ocular ageing. However, the association between BMR and presbyopia remains underexplored. Therefore, using a two-sample Mendelian randomisation (MR) approach, the present study aimed to evaluate the potential causal relationship between BMR and presbyopia in individuals of European ancestry. Methods: Instrumental variables comprised single-nucleotide polymorphisms associated with BMR at genome-wide significance (p < 5.0 × 10⁻⁸), derived from genome-wide association study summary statistics from the UK Biobank. Summary statistics for presbyopia were obtained from the FinnGen project. Causal estimates were primarily assessed using the inverse-variance weighted method and further evaluated using the weighted median method, MR–Egger regression, and the MR–Pleiotropy Residual Sum and Outlier test. Results: Genetically predicted higher BMR, expressed per 1-standard-deviation increase on the inverse-rank-normalised scale, was associated with lower odds of presbyopia. The inverse-variance weighted analysis yielded an odds ratio (OR) of 0.79 (95% confidence interval [CI]: 0.67–0.93; p = 0.004), with a directionally consistent estimate from the weighted median analysis (OR = 0.76, 95% CI: 0.59–0.99; p = 0.045). MR–Egger and SIMEX-corrected MR–Egger analyses yielded estimates in the same inverse direction, although their 95% CIs included the null (OR = 0.73, 95% CI: 0.50–1.05; p = 0.093 and OR = 0.71, 95% CI: 0.47–1.06; p = 0.092, respectively). The MR-PRESSO global test was nominally significant (p = 0.049), although no individual outlier was identified. In additional MVMR analyses incorporating BMI or standing height, the direct effect of BMR was not statistically significant in either model. Conclusions: These findings suggest a potential inverse association between higher genetically predicted BMR and the odds of presbyopia. However, MVMR analyses did not support a direct effect of BMR independent of BMI or height, warranting caution in interpreting the observed association as specific to metabolic rate. Further studies are required to validate these results and clarify the underlying biological mechanisms. Full article

A Transmission Control Protocol (TCP) throughput map represents communication quality over road networks and supports communication-aware applications in intelligent transportation systems. Maintaining such a map online is challenging because vehicular measurements are sparse and unevenly distributed, network conditions vary rapidly, and sensing-budget constraints limit the number of vehicles from which onboard communication measurements can be uploaded at each time step. This work addresses online TCP throughput map maintenance under sparse vehicular observations and sensing-budget constraints. To support budget-constrained sensing, we combine discoverability-guided vehicle selection and probabilistic map updating within a digital twin (DT)-assisted vehicular sensing architecture. The resulting sensing-and-mapping method, referred to as Discoverability-aware and Statistical Mapping (DISMAP), maintains a spatio-temporal discoverability map to characterize historical sensing coverage and select vehicles that improve the coverage of under-represented regions. It then uses Gaussian Process Regression (GPR) as a probabilistic mapping engine to estimate the mean TCP throughput and predictive standard deviation, where the standard deviation is adjusted using local vehicle density. Simulation results show that DISMAP reduces the mean absolute error (MAE) and mean standard deviation (MSTD) by up to 23.7% and 37.5%, respectively, and achieves a prediction-interval miss rate (PIMR) of 0.048, which is close to the nominal value of 0.05. These results indicate a favorable balance among prediction accuracy, interval sharpness, calibration, and spatial representativeness across different traffic-density conditions. Full article

Real data such as road traffic mortality rates and lifetime observations are often zero-inflated and right-skewed. The zero-inflated two-parameter Rayleigh (ZITR) distribution is employed to model such data in this study. The coefficient of variation (CV) is a statistical measure that is used to quantify the relative dispersion of a population, by comparing the standard deviation with the mean. It is widely used to evaluate variability and facilitate comparisons among datasets with different scales or measurement units. This study develops and evaluates seven methods for constructing confidence intervals for the single CV of the ZITR distribution. Three proposed approaches, including Bayesian Markov chain Monte Carlo (MCMC), Bayesian highest posterior density (HPD), and approximate normal (AN) methods, are compared with three existing approaches: generalized confidence interval (GCI), percentile bootstrap (PB), and bootstrap with standard error (BS). Monte Carlo simulations are employed to assess the efficacy of these methods in terms of expected length (EL) and coverage probability (CP). The simulation results show that the HPD method gives acceptable CP with shorter interval lengths than other methods. Moreover, the proposed methods are illustrated with road traffic mortality rates per 100,000 population collected in January 2026 from the Phichit, Suphan Buri, and Prachuap Khiri Khan provinces in Thailand. The results indicate the applicability of the proposed methods for analyzing zero-inflated and right-skewed data in this real-data example. Full article

High renewable penetration makes imbalance settlement inseparable from the physical decisions governing reserve procurement and flexibility activation. This paper develops a decision-focused learning-based optimization framework that trains renewable-deviation and flexible-resource deliverability representations through downstream dispatch, reliability, and settlement consequences. The mathematical contribution is a settlement-aware learning objective that couples learned uncertainty, resource-time credible-capacity certification, network-constrained multi-stage dispatch, and counterfactual marginal-contribution allocation while retaining an exact revenue-adequacy identity. The 33-node Zhangjiakou-type regional case uses 15 min intervals and comprises five resource classes: independent storage, data-center flexibility, industrial adjustable load, commercial demand response, and electric-vehicle aggregation. Relative to a fixed-ratio reserve rule, the proposed method lowers the regional balancing cost from 950 to 618 thousand USD (34.9%), achieves 97.8% renewable accommodation, limits the shortage probability to 0.7%, and attains a settlement-fairness index of 0.92. The framework solves a 500-asset instance in 118 s. External validation uses 4027 half-hour observations from the 2025 Elexon/BMRS market, including measured wind and solar output, day-ahead forecasts, load, imbalance prices, and procured-reserve prices. On the 1487-interval December test set, the proposed model reduces the replay cost from 2953.3 to 2598.2 thousand GBP (12.0%), decreases the shortage-interval frequency from 4.64% to 1.28%, and reaches 99.74% renewable accommodation. Comparisons with forecast-then-optimize, Wasserstein distributionally robust optimization, off-policy reinforcement learning, and graph-based behavioral cloning establish that the improvement comes from jointly learning which uncertainty matters for dispatch and which flexible capacity is deliverable. Full article

A robust fault-tolerant predictive asynchronous switching control method based on robust positively invariant sets is proposed for multi-phase batch processes subject to actuator faults, unknown disturbances, and asynchronous switching. To attenuate the effect of unknown disturbances, a min–max performance index is constructed, by which the robust control problem is formulated as a min–max optimization problem under worst-case disturbance conditions. To improve fault tolerance, robust positively invariant sets are introduced into the controller design so that the system states can remain within a constraint-satisfying feasible region under admissible actuator faults. Moreover, an online pre-switching mechanism is developed to address the phase mismatch between the system phase and controller. By updating the switching timing according to the real-time operating state, the controller can be adjusted to the corresponding control law before the system enters the next phase, thereby reducing the mismatched interval and suppressing state deviation. A case study on the injection and holding phases of the injection molding process shows that the proposed method improves tracking accuracy and operational smoothness under actuator faults, unknown disturbances, and asynchronous switching, demonstrating its effectiveness and applicability. Full article

Fractures exhibit complex spatial structures and multiscale geometric characteristics, and their accurate characterization is fundamental to reservoir evaluation, fluid migration analysis, and rock mechanics. To address the limitations of single-scale local fractal methods in simultaneously capturing fracture details and global structures, as well as the dependence of supervised learning on labeled data, this study proposes an unsupervised fracture identification method integrating Complexity-Adaptive Box-Height Differential Box-Counting (CABH-DBC) with a self-organizing map (SOM). Local fractal features are extracted using fixed multiscale windows, while the box height along the gray-level dimension is adaptively refined according to the local grayscale standard deviation. The multiscale features are then fed into the SOM for clustering, with grayscale information assisting in fracture-cluster determination. Experiments on borehole image logs from ten depth intervals of the CCSD main borehole yield mean F1 and IoU values of 0.659 and 0.493, respectively. Compared with DBC-Kmeans, the proposed method improves F1 and IoU by 39.0% and 58.0%, respectively; compared with DBC-SOM, the strongest baseline in this study, the improvements are 16.6% and 24.8%. Ablation experiments further demonstrate the complementary contributions of complexity-adaptive box-height refinement, fixed multiscale fractal features, and SOM clustering. Full article

High-precision tunnel breakthroughs depend critically on the north-seeking accuracy of maglev gyroscopes. However, external disturbances during underground construction often introduce abrupt jumps into rotor current signals, significantly reducing the orientation reliability. Existing signal-processing methods either require manually defined segmentation windows or apply identical denoising strategies to both stationary and disturbed signal intervals, resulting in limited adaptability and suboptimal denoising performance. To overcome these limitations, this study proposes an improved rotor current denoising algorithm based on the MAF-ARIMA framework by incorporating the Bayesian ensemble algorithm for abrupt change, seasonality, and trend (BEAST) and an optimized wavelet transform (OWT). First, the BEAST is employed to automatically detect the structural change point of the rotor current signal, enabling the adaptive segmentation of stationary and jump intervals without manual intervention. Subsequently, empirical mode decomposition is performed, and the OWT applies different denoising parameters to the dominant components of the stationary and jump segments according to their distinct fluctuation characteristics. Finally, moving-average smoothing is adopted to preserve the signal continuity at the segmentation boundary, while the autoregressive integrated moving average (ARIMA) model reconstructs the missing trend component of the jump interval to obtain the complete denoised signal. Comparative experiments using 12 field-collected rotor current datasets demonstrated that the proposed method reduced the standard deviation of the denoised signal by 70.96% and the absolute azimuth error by 50.36% compared with the raw signal, outperforming the optimized Hilbert–Huang transform, HSA-KS, and the original MAF-ARIMA algorithm. By introducing adaptive change-point detection and segment-specific denoising into the existing MAF-ARIMA framework, the proposed method significantly improves the adaptability and denoising performance of maglev gyro rotor current processing under complex tunnel construction environments while preserving the signal continuity and reconstruction accuracy. Full article

Background/Objectives: Pretreatment sarcopenia predicts adverse outcomes in head and neck squamous cell carcinoma (HNSCC). The conventional L3 skeletal muscle index (SMI) requires abdominal imaging, whereas the predictive value of the more accessible C3 muscle index for acute treatment-related complications remains uncertain. We compared pretreatment L3 SMI and the C3 index for identifying patients at risk of severe oral intake impairment, radiation interruption, and a longer treatment-to-discharge interval during definitive chemoradiotherapy. Methods: This retrospective single-center study included 55 patients with HNSCC treated with 80 mg/m² cisplatin every three weeks and 66 Gy intensity-modulated radiotherapy. Pretreatment computed tomography (CT) was analyzed using deep-learning-based automated segmentation. Firth-penalized logistic and multivariable linear regression were adjusted for age, sex, performance status, body mass index, and stage, with estimates standardized per 1-standard-deviation (SD) increase. Results: L3 SMI was associated with severe oral intake impairment (adjusted odds ratio per SD, 0.248; 95% CI, 0.053–0.891) and the treatment-to-discharge interval (adjusted β, −6.23 days; 95% CI, −11.89 to −0.58); the C3 index was not associated with any outcome. The standardized coefficients differed for oral intake impairment (p = 0.014) but not for other outcomes. Each binary outcome comprised only 10 events. Conclusions: L3 SMI was associated with nutritional vulnerability, whereas the fully automated C3 index was not; in an exploratory direct comparison, the two indices differed for severe oral intake impairment. These findings are hypothesis-generating and require confirmation in larger, multicenter cohorts. Full article

Petroleum engineering projects face complex risk environments. Existing risk control systems often only provide overall risk safety thresholds. They lack effective quantitative interval estimation methods. Moreover, translating quantitative analysis findings into actionable on-site management instructions remains challenging. Symmetry serves as a core analytical perspective for cutting-edge research in control theory and system engineering. Feedforward and feedback controls are functionally complementary and sequentially cascaded, featuring intrinsic complementary symmetry. From the perspective of feedforward–feedback symmetry, this study constructs a GA-LLM hybrid framework combining genetic algorithm (GA) and large language models (LLMs) to address the above shortcomings. This framework is jointly composed of four collaboratively functioning modules, comprising the risk status input module, GA feedforward control module, retrieval-augmented generation (RAG) knowledge retrieval module, and the LLM feedback control strategy-generation module. In this framework, the GA serves as the feedforward controller, which computes the joint inscribed control box for each risk factor offline based on the risk relationship model established by the Back Propagation (BP) neural network. The RAG-enhanced LLM serves as the feedback controller, dynamically generating structured risk control instructions based on deviations. Case validation results demonstrate that the BP neural network achieved excellent performance with an R² of 0.99575 under leave-one-out cross-validation. The GA successfully solved the joint control box for the 14 risk factors, achieving a 100% joint constraint satisfaction rate for any combination within the box. The RAG retrieval module achieved a Recall@5 of 0.8933, MRR@5 of 0.7367, nDCG@5 of 0.7505, and Success@5 of 1.000. Ablation experiments show that the RAG-LLM scheme outperformed both the LLM without RAG scheme and the rule-based template scheme across four dimensions, with an inter-rater reliability ICC(2,1) of 0.719, reaching a moderate reliability level. This study integrates the quantitative optimization capability of GA with the semantic generation capability of LLM, enabling the transformation from the joint control box to executable management instructions. It not only provides a practical tool for petroleum engineering risk management but also offers new insights for the design of intelligent control systems from the perspective of feedforward–feedback symmetry. Full article

Background/Objectives: Magnesium (Mg) deficiency has been linked to various adverse health outcomes; however, its association with metabolic dysfunction-associated steatotic liver disease (MASLD) has not been fully elucidated. This study examined the association of serum Mg levels with MASLD in Korean adults. Methods: We analyzed a nationwide sample of 4952 adults in South Korea. Serum Mg levels (mg/dL) were measured. MASLD was defined as a hepatic steatosis index score of ≥36 together with the presence of cardiometabolic risk factor, including overweight/obesity, high fasting glucose, high blood pressure, high plasma triglycerides, or low plasma HDL cholesterol. The association between serum Mg levels and MASLD was examined using logistic regressions. Odds ratios (ORs) and 95% confidence intervals (CIs) were determined. Results: The mean (standard deviation [SD]) serum Mg level was 2.13 (0.15) mg/dL. The prevalence of MASLD was 25.7%. After adjusting for the sociodemographic variables, the OR (95% CI) for the association between a 1–SD increment in serum Mg levels and MASLD was 0.90 (0.84–0.96, p = 0.004). Conclusions: This nationally representative cross-sectional study found that serum Mg levels were inversely associated with the likelihood of MASLD among adults in South Korea. Full article

Background: The Oxford Hip Score (OHS) is a widely used hip-specific patient-reported outcome measure (PROM) following primary total hip arthroplasty (THA). This measure is calculated from answers to a 12-item questionnaire and can provide indices that indicate clinically meaningful success, such as patient acceptable symptom state (PASS). There were three purposes of the present single-cohort retrospective study: first, to determine the threshold PASS; second, to determine the influence of patient characteristics (age, gender, and body mass index (BMI)) and preoperative OHS and other PROMs of patients (namely, EQ-5D-3L, EQ-VAS, and pain VAS) who did not achieve this threshold; and third, to establish whether failure to achieve the PASS could still be associated with meaningful clinical improvement. Methods: This single-centre retrospective cohort study was conducted over 10 years from January 2013 to December 2022, during which 3997 patients completed pre- and postoperative OHS. The mean age was 68.3 years (standard deviation 11.7), and there were 2375 (59.4%) females in the cohort. Patient satisfaction was used as the anchor to define the PASS. Results: An OHS at 12 months postoperative of ≥36 points was identified as the threshold PASS (area under the curve 88.5%, 95% confidence interval [CI] 87.0 to 90.1, p < 0.001). Overall, 3002 (75.1%) achieved the PASS at 12 months. Lower BMI (odds ratio [OR] 0.96, 95% CI 0.95 to 0.98, p < 0.001) and higher preoperative OHS (OR 1.05, 95% CI 1.03 to 1.06, p < 0.001), EQ-5D (OR 1.88, 95% CI 1.33 to 2.65, p < 0.001) and EQ-VAS (OR 1.02, 95% 1.01 to 1.02, p < 0.001) were independently associated with achieving the PASS at 12 months. A total of 995 patients (24.9%) did not achieve this threshold. Patients not achieving the PASS had clinically meaningful worse (p < 0.001) postoperative outcomes and improvement relative to baseline in OHS, EQ-5D and EQ-VAS when compared to those achieving a PASS. Patients achieving the PASS were more likely to be satisfied with their THA (OR 16.8, 95% CI 12.8 to 22.1, p < 0.001); however, patients not achieving the PASS still had significant (p < 0.001) improvements relative to baseline in their OHS (9.6, 95% CI 9.0 to 10.2), EQ-5D (0.254, 95% CI 0.229 to 0.278), EQ-VAS (1.4, 95% CI −0.3 to 3.1) and pain VAS (8.2, 95% CI 6.1 to 10.3) and were more likely to be satisfied (70.8%) than not (29.2%). Conclusions: An OHS of ≥36 at 12 months was defined as the PASS. In total, 995 patients (29.4%) did not achieve this threshold, and in this group, patients were more likely to be female and have a high BMI and low preoperative OHS, EQ-5D, EQ-VAS, and pain VAS. Despite not achieving the PASS, they reported meaningful clinical improvements in OHS, EQ-5D, and pain VAS, and were satisfied with their THA (70.8%). Full article

Background/Objectives: Sex-based differences in asbestos exposure and tumor biology in pleural mesothelioma (PM) remain incompletely characterized. As an expert tertiary center, we sought to systematically characterize asbestos exposure by sex and assess biomarker prognostic relevance in surgically treated pleural mesothelioma. Methods: We conducted a retrospective, single-center cohort encompassing all patients who underwent surgical intervention for PM at our institution. Demographic characteristics, asbestos exposure history, tumor biomarker profiles, and survival outcomes were extracted from this database and supplemented by review of the electronic medical record. Sex differences in asbestos exposure and biomarker expression were assessed using Fisher’s exact test on an available-case basis, while logistic regression was used to identify predictors of documented asbestos exposure. Overall survival was estimated using Kaplan–Meier analysis; independent associations between biomarkers and mortality were determined via multivariable Cox proportional hazards models adjusted for age, sex, and histological subtype. Results: Between 2015 and 2024, 106 patients (75% males) underwent surgical intervention for PM, with a median follow-up of 4.4 years. The mean age at surgery was 67.5 years (Standard Deviation [SD], 11). The cohort predominantly consisted of patients with epithelioid histology (n = 86, 81%), followed by biphasic (n = 16, 15%), and sarcomatoid (n = 4, 4%) subtypes. Thirty (28.3%) were alive at last follow-up. Median overall survival was 15.6 months (95% Confidence Interval [CI], 13.8–25.4 months), and 5-year survival was 17.8%. Of 92 patients with available exposure data, 66 (72%) had documented exposure histories. Although most of these exposures were occupational (46, 78%), a striking disparity was observed between occupational exposures in males vs. females (44 [86%] vs. 2 [25%]; n = 8 classifiable females; p < 0.001). Multivariable analysis demonstrated that male sex was associated with increased odds of asbestos exposure (Adjusted Odds Ratio [OR_adj], 5.45; 95% Confidence Interval [CI], 1.90–16.35). PD-L1, Ki-67, or BAP1 expression were not associated with sex, age, or asbestos exposure. High Ki-67 (>15%) and PD-L1 (>15%) were associated with a four-fold and three-fold increased risk of mortality (Hazard Ratio_adj [HR_adj], 4.33 [95% CI, 1.85–10.13] and HR_adj, 2.80 [95% CI, 1.39–5.64], respectively), whereas BAP1 loss was not (HR_adj, 0.80; 95% CI, 0.45–1.44). Conclusions: Our findings underscore the potential value of sex-sensitive screening protocols that more comprehensively assess non-occupational asbestos exposure pathways and offer additional evidence regarding the prognostic relevance of PD-L1, Ki-67, and BAP1 in surgical patients. Full article

Background/Objectives: In Lebanon, the economic crisis since 2019 has severely strained healthcare infrastructure, yet its impact on cancer treatment adherence remains incompletely characterized. This study assessed factors associated with treatment delay and regimen modification to identify barriers to optimal cancer care among Lebanese patients. Methods: This multicenter, cross-sectional study was conducted in five tertiary hospitals in Beirut, Lebanon, between 13 June 2024, and 27 June 2025. Demographic, clinical and treatment data were collected via a structured questionnaire. Bivariate and multivariable binary logistic regression analyses were performed to identify independent predictors of treatment delay (>2 weeks) and incomplete treatment regimens. Adjusted odds ratios (aORs) with 95% confidence intervals (CIs) are reported. Results: A total of 244 patients were enrolled in the study. The study population was predominantly female (59.0%) and aged ≥60 years (60.2%). Advanced disease was common, with 50.4% of patients presenting with stage IV disease. Financial vulnerability was widespread: 72.6% reported monthly household incomes below USD 1000, 88.1% reported a decline in income due to the economic crisis, and 39.3% relied on unstable income sources. Overall, 52.5% of patients experienced treatment initiation delays, and 9.0% received incomplete or dose-reduced regimens. On multivariable analysis, reliance on unfixed income independently predicted treatment delay (aOR 2.55, 95% CI 1.42–4.59; p = 0.002), lack of health insurance (aOR 1.91, 95% CI 1.08–3.39; p = 0.026) and pre-treatment surgical costs also increased the odds of delay (aOR 9.03, 95% CI 2.57–31.7; p = 0.001). For incomplete treatment, unfixed income remained an independent predictor (aOR 2.09, 95% CI 1.16–5.79; p = 0.015). A lack of insurance (aOR 5.62, 95% CI 1.22–25.9; p = 0.027) and high laboratory costs (>USD 150 per session) were also associated with incomplete regimens (aOR 1.62, 95% CI 1.02–2.57; p = 0.041). Conclusions: In Lebanon’s crisis context, financial instability was a key factor associated with deviations in cancer treatment. These findings highlight the need for strengthened insurance coverage and subsidization of diagnostic and treatment-related costs to ensure timely and continuous oncologic care. Full article