Search Results (399)

The cooperatives combine a pyramidal structure, in which few members concentrate decision-making power, with ownership dispersed among a broad membership base. This configuration favors adverse selection and moral hazard arising from information asymmetry between managers and stakeholders, which legitimacy theory suggests may be reduced through sustainability disclosure. The study advances the literature by combining three elements not yet integrated in research on cooperatives: the cultural pillar as an autonomous dimension of sustainability, disaggregated analysis by pillar, and risk-adjusted financial performance. The level of sustainability disclosure was analyzed from a stakeholder perspective, along with its association with financial performance. Forty-four expert-validated indicators were applied to 39 cooperatives listed in the 2022 World Cooperative Monitor that published complete reports over the 2020–2022 triennium, resulting in 117 observations, estimated by fixed effects with cluster-robust standard errors. Mean disclosure was 0.52, led by the environmental pillar (0.70), followed by the economic (0.62), social (0.47), and cultural (0.33) pillars. The aggregate index showed no statistically significant association with risk-adjusted financial performance, either for ROA or for ROE, whereas the social dimension remained positive and significant regardless of the metric used. This pattern is consistent with the propositions of legitimacy theory regarding the social pillar, although the underlying mechanisms of information asymmetry reduction and legitimacy strengthening were not directly measured in this study. Full article

Urban park assessment typically emphasizes vegetation quantity, yet spaces with similar greenness may still produce different visual experiences. This study aimed to identify the structural characteristics of horticultural configurations and examine their incremental predictive value for multidimensional visual perception. A total of 500 local landscape scenes were selected from 10 urban parks in Chengdu and presented as standardized photographs. One hundred and fifty participants rated 17 visual attributes on a seven-point scale. Landscape structure metrics, principal component analysis, cluster analysis, cross-park validation, and SHAP analysis revealed that horticultural configurations varied primarily along three gradients: vegetation continuity–fragmentation, geometric complexity, and patch size. These gradients formed three representative states: dense woody, open heterogeneous, and impervious fragmented configurations. Visual perception was organized into three functional domains: restorative immersion, spatial openness, and affective richness. Compared with models containing only the NDVI and greenspace coverage, adding configuration metrics increased the cross-park predictive R² by 0.257, 0.181, and 0.241 for the three perceptual domains, respectively. Vegetation aggregation and connectivity, mean patch area, and shape complexity were the primary predictors of the respective domains and exhibited stable nonlinear response patterns. This study extends park assessment from vegetation quantity to spatial organization mechanisms, providing an evidence base for perception-oriented horticultural design and helping reduce costly trial-and-error and inefficient investment in park renewal. Full article

The effects of transcutaneous electroacupuncture stimulation (TEAS) on large-scale brain function remain insufficiently characterized. This study employed a graph-theoretical approach to analyze electroencephalogram (EEG) data from 48 healthy participants in the Pilot-6 TEAS study. Participants received sham (0 pps), 2.5 pps, 10 pps, and 80 pps stimulation during baseline, stimulation, and recovery phases. Functional connectivity was assessed using coherence and the weighted phase-lag index, followed by calculation of global and nodal graph measures from thresholded weighted undirected sensor-level networks. Descriptive analysis indicated potential frequency-related differences in EEG network organization. The 2.5 pps condition exhibited the highest average degree, whereas the 80 pps condition demonstrated the highest average clustering coefficient. At 10 pps, sensor-level maps revealed a distinct frontal–central betweenness-centrality pattern. Although 48 participants provided usable EEG data for descriptive analysis, only 3 participants had complete matched graph-metric data for all four stimulation conditions, limiting repeated-measures statistical validation. After correction for multiple comparisons, no statistically significant frequency-related effects were observed, and nodal hub differences were not independently confirmed. Consequently, these patterns should be interpreted as descriptive and exploratory rather than established group-level effects. These findings indicate that graph-theoretical EEG analysis may facilitate the identification of candidate network features for future investigations of TEAS-related brain network organization. Full article

Accurately assessing the fatigue levels of air traffic controllers is crucial for reducing human errors in ATC and ensuring the safe and orderly operation of the civil aviation transportation system. In remote tower scenarios, air traffic controllers’ work environments and task interaction modes differ significantly from those in traditional towers, and traditional fatigue detection approaches relying on physiological monitoring can cause intrusive disruptions to ATC operations. To overcome these limitations, this study proposes a scenario-based, non-invasive assessment framework for accurate and low-interference fatigue recognition. Taking three key scenario elements (traffic load, main operation screen brightness, and core work area illuminance) as the basis for measuring fatigue, the framework bridges the mapping from scenario elements to fatigue status, thereby enabling the transition of assessment inputs from physiological metrics to scenario features. In this mapping, fatigue labels are determined using a fusion strategy. Specifically, objective fatigue labels are derived from optimal wave features extracted from electroencephalogram data using one-way analysis of variance (OW-ANOVA), which are then fused with subjective labels based on the Karolinska Sleepiness Scale (KSS) self-reports through fuzzy C-means (FCM) clustering. Ultimately, a hybrid intelligent classification model integrating the Gannet optimization algorithm (GOA) and random forest (RF) is constructed to perform the primary assessment task. The experimental results indicate that the proposed framework achieves a recognition accuracy of 95.00%, outperforming six other commonly used classification or combination models. Ablation experiments and robustness tests validate the effectiveness of the fused labeling strategy and GOA modules, as well as the method’s excellent stability in resisting data noise. Furthermore, feature interpretability analysis reveals the quantitative influence of the three core fatigue drivers used. The research findings confirm the feasibility of non-invasive fatigue assessment for remote tower controllers leveraging scenario-based elements, which can offer intelligent decision support for controller shift scheduling, visual environment optimization, and targeted safety interventions. Full article

Background: Suicide is a leading cause of preventable death, yet machine learning (ML) analyses of forensic (medico-legal) suicide data are scarce and, to our knowledge, absent for Romania. Population-based forensic registries offer exhaustive, autopsy-confirmed coverage that is structurally distinct from clinical or civil death-registration data. We applied supervised and unsupervised ML to a complete regional medico-legal suicide registry to profile the method of death and to identify latent victim subgroups of preventive relevance. Methods: We analysed 395 consecutive suicide deaths (Galați and Brăila counties, ≈750,000 inhabitants; 2018–2024). Two supervised classifiers—L2-regularised logistic regression (LR) and random forest (RF, 200 trees)—were trained to discriminate hanging from other methods, using eleven sociodemographic and clinical predictors, and evaluated by 10-fold stratified cross-validation. Given severe class imbalance, the area under the ROC curve (AUC) was the primary metric. Model hyperparameters were fixed a priori, and no class-imbalance correction was applied; both decisions are pre-specified and justified in the Methods. Robustness was assessed by stratified non-parametric bootstrap confidence intervals for the odds ratios, a tipping-point sensitivity analysis for the undocumented clinical fields, and Ward-linkage hierarchical clustering as an independent partitioning check. Predictor importance was quantified by out-of-bag (OOB) permutation importance and Spearman correlations. Unsupervised structure was assessed by K-means clustering (k = 2–7), with the optimal solution selected by the average silhouette coefficient and the elbow (WCSS) criterion. Reporting followed TRIPOD+AI and STROBE. Results: The study population was predominantly male (87.1%) and rural (73.2%), with a mean age of 54.1 years; hanging accounted for 94.2% of deaths—far above the European average (≈50%). RF achieved AUC = 0.865 ± 0.181 and LR AUC = 0.847 ± 0.192, both within the “excellent” discrimination band; sensitivity was very high (0.995–0.997) and specificity was limited (0.233–0.367), an expected consequence of imbalance. Prior suicide attempts (OOB importance 0.959; Spearman ρ = −0.549, p < 0.001; OR = 0.496, 95% CI 0.25–0.72) and the presence of a suicide note (importance 0.575; ρ = −0.439, p < 0.001; OR = 0.549, 95% CI 0.34–0.76) were the dominant predictors. K-means identified two well-separated clusters (silhouette = 0.707), and the partition was reproduced exactly by Ward-linkage hierarchical clustering (adjusted Rand index = 1.000). Cluster 2 (n = 19; 4.8%) was a clinically distinct, younger subgroup (42.4 vs. 54.7 years) characterised by prior attempts (57.9% vs. 0%), suicide notes (68.4% vs. 0%), higher psychiatric comorbidity (52.6% vs. 30.9%) and lower hanging proportion (36.8% vs. 97.1%)—an exploratory, hypothesis-generating profile of recurrent suicidal behaviour with documented prior contact with the medical or medico-legal system. The principal findings were stable across all plausible degrees of clinical under-documentation in the tipping-point sensitivity analysis. Conclusions: ML applied to a complete forensic suicide registry reproduced known regional epidemiology and, beyond classical statistics, isolated an exploratory but clinically coherent high-risk subgroup of direct relevance to the audit of structured post-attempt follow-up. This is, to our knowledge, the first ML study of Romanian forensic suicide data and supports integrating ML into medico-legal research and into the regional targeting and audit of existing post-attempt follow-up provision. Full article

Background/Objectives: Auditory verbal hallucinations (AVH) are clinically heterogeneous experiences that may occur across psychiatric, neurological, sensory, and non-clinical contexts. This controlled pilot study investigated multimodal structural and functional MRI features associated with persistent AVH in a psychiatric clinical population, recruited from outpatient psychiatric clinics and diagnosed with schizophrenia, schizoaffective disorder, or bipolar disorder with psychotic features. Neurological, sensory, and non-clinical presentations of AVH were not included. Methods: This observational controlled pilot study included 14 participants with persistent auditory verbal hallucinations and 15 age- and sex-matched healthy controls. Participants with AVH had experienced the current persistent hallucinatory phase for a mean of 3.1 ± 1.6 years (range of 1–7), with a mean overall psychiatric illness duration of 12.4 ± 4.8 years. Independent component analysis assessed resting-state functional connectivity, BrainVoyager QX measured cortical thickness, and diffusion tensor imaging (DTI) evaluated white matter microstructure. Multiple comparisons were controlled using false discovery rate correction followed by 5000-iteration Monte Carlo cluster-extent correction for fMRI and Monte Carlo cluster correction for whole-brain structural metrics. Results: Participants with AVH demonstrated increased functional connectivity across default mode network (DMN) hubs (precuneus, inferior frontal, and parahippocampal gyri) and superior temporal regions. Whole-brain cortical thickness analysis revealed no significant group differences; however, secondary exploratory analyses of six regions previously implicated in AVH showed cortical thinning in participants with AVH relative to the controls after FDR correction. DTI revealed no group differences surviving whole-brain permutation correction (TFCE, FWE-corrected p < 0.05); exploratory uncorrected findings are reported as hypothesis-generating. Conclusions: This pilot study identifies structural and functional network differences between medicated individuals with persistent AVH and healthy controls, centred on frontotemporal and default mode networks. Because no psychiatric control group without AVH was included, these differences cannot be attributed specifically to AVH as opposed to the underlying psychiatric disorders or their treatment. No diffusion findings survived whole-brain permutation correction; exploratory uncorrected results are reported but are not incorporated into these conclusions. Collectively, these findings identify candidate imaging markers that require validation against psychiatric control groups. Full article

Aircraft fuselage corrosion inspection requires both image-domain recognition and metric physical-surface localization for maintenance execution. This study develops a camera-calibrated vision measurement framework that combines PWDE-YOLOv8n-based corrosion perception, original-image coordinate restoration, lens-distortion compensation, ray-based surface mapping, and detector-center bias correction. The perception dataset comprised 2143 images and 5941 corrosion annotations and was partitioned at the physical-specimen, acquisition-session, or source-group level into 1500 training images, 429 validation images, and 214 independent detector-test images. Detailed physical localization was evaluated on a six-image metrology cohort acquired in six sessions, containing 21 corrosion boxes and 84 axial coordinates. A six-fold leave-one-image-out procedure was adopted; in each fold, the center-shift parameters were estimated from the other five images and applied unchanged to the held-out image. The proposed method achieved a mean absolute axial error of 0.641 mm (95% image-cluster bootstrap confidence interval: 0.571–0.708 mm), an RMSE of 0.809 mm, a maximum error of 3.262 mm, and a projected physical-plane bounding-box IoU of 87.12%. The expanded uncertainty of the manually established reference coordinates was 0.374 mm at k = 2, and Monte Carlo propagation produced a mean absolute error of 0.656 mm with a 95% interval of 0.618–0.693 mm. The proposed method reduced the MAE by 97.91% relative to local pixel-to-millimeter scaling and by 70.58% relative to conventional calibrated camera mapping, while producing accuracy comparable to planar homography mapping. Within ρ ≥ 1500 mm, W ≤ 150 mm, and θ ≤ 20°, the estimated curvature-induced additional axial error did not exceed 0.683 mm. A separate ten-image deployment evaluation produced a mean axial error of 2.448 mm and an average processing time of 53.35 ms/image. Full article

Background/Objectives: The proximity of the maxillary sinus floor to the tooth apices is critical for dental procedures, so preoperative evaluation is crucial for preventing complications. This study aims to examine the accuracy of panoramic radiographs in determining the relationship between the maxillary sinus and teeth, in comparison with cone-beam computed tomography (CBCT), which serves as the reference standard, and to develop a cluster-adjusted clinical prediction model to assist clinicians in identifying patients who may benefit from CBCT imaging. Methods: A total of 3436 teeth were evaluated using CBCT and panoramic radiographs for the relationship between root tips and sinuses. The McNemar–Bowker test and the R program were used to assess how accurate panoramic radiographs are by comparing them to CBCT imaging. The performance metrics of panoramic radiography were calculated, and the generalized estimating equation (GEE) logistic regression model and the interactive risk calculator were developed. Results: The McNemar–Bowker test indicated a statistically significant systematic difference between the two modalities (χ² = 190.01, p < 0.001); the overall three-category agreement was 82.2% (cluster-corrected 95% CI: 80.4–84.0%), corresponding to a dichotomized (protrusion vs. non-protrusion) accuracy of 87.6%. A cluster-corrected GEE logistic regression model, including panoramic classification, tooth type, age, and sex and developed and internally evaluated in this single-center cohort, predicted root protrusion identified using CBCT with high apparent discrimination (AUC = 0.948; cluster-based bootstrap 95% CI: 0.941–0.955, 1000 resamples; optimism-corrected AUC = 0.948) and satisfactory apparent calibration. Conclusions: This model, developed and evaluated internally within a single-center cohort, has the potential to serve as a clinical decision-support tool to help prioritize the need for CBCT in patients pre-evaluated with panoramic radiography. However, its performance reflects apparent, in-sample estimates, and external validation in independent populations is required before it should be considered for routine clinical use. Full article

Background: Q&A corpora generated through university student–AI mental health support tools may reveal how physical activity (PA) and psychosocial themes are represented in support-oriented text. However, the absence of individual-level demographic metadata and the pooling of prompt and response fields limit attribution of any expression to a particular speaker, and the corpus describes a specific student population rather than a general or clinical one. Objective: This exploratory study described PA-, sport-, physical education (PE)-, body-, lifestyle-, and emotion-related patterns in a large corpus of university student–AI mental health exchanges collected through an institutional counselling platform. Methods: This study analysed 209,715 paired prompt–response records as combined exchange-level units using a BERTopic-based computational text-mining workflow. The full corpus was used for the main 18-topic model and overlapping dictionary analyses. After secondary data-quality filtering, 178,062 eligible exchanges formed the sampling frame from which a systematic sample of 10,000 exchanges was drawn for a separate complementary BERTopic and scenario-mapping analysis. The workflow used Qdrant/bge-small-zh-v1.5 embeddings, NFKC normalisation, an archived stop-word list, UMAP (n_neighbors = 15, n_components = 5, min_dist = 0.0, cosine metric, seed = 42), HDBSCAN (min_cluster_size = 300, min_samples = 10, Euclidean metric, EOM), c-TF-IDF topic representations, overlapping dictionary screens, and stability testing across seeds 42, 52, and 62. Results: A student/school/family-context lexical screen matched 83,215 exchanges (39.68%), and a broad PA/body/lifestyle screen matched 82,464 exchanges (39.32%). These overlapping indicators describe topical co-occurrence and do not establish PA behaviour or which party to the exchange produced a given term. Eighteen corpus-level themes were retained. In the 10,000-exchange analysis, 13.11% of exchanges matched a narrow movement-related expression screen, with the highest within-topic rate in the sample topic labelled emotional outburst and relaxation regulation (51.09%). Conclusions: The findings describe exchange-level lexical and topic patterns in student–AI interactions rather than actual PA behaviour, intervention delivery, clinical efficacy, or population prevalence, and they do not identify which party introduced the language. The mapping to autonomy, competence, relatedness, and emotional regulation is a post hoc interpretive lens, offered as a hypothesis to inform future, prospectively validated design work in PE and digital mental health support rather than as a demonstrated result. Full article

Anthropogenic climate change has disrupted the global hydrological cycle, increasing compound extreme events like Dry–Wet Abrupt Alternations (DWAAs). Regarding the Mediterranean Basin, Greece is highly susceptible to these abrupt hydroclimatic shifts, which frequently overwhelm reactive disaster management. This study quantifies the spatiotemporal dynamics of DWAA events across Greece from 1990 to 2024. Using the 1-month Standardized Precipitation Evapotranspiration Index (SPEI-1) from ERA5 reanalysis, transitions were classified into dry-to-wet (DW) and wet-to-dry (WD) across moderate (±1.0), severe (±1.5), and extreme (±2.0) thresholds. Core physical metrics (duration, severity, and intensity) were evaluated using Anselin Local Moran’s I (LISA) and Mann–Kendall tests to identify spatial hotspots and temporal trends. Results revealed a spatially decoupled hazard regime dictated by topography and atmospheric mechanics. Severe DW transitions primarily manifest as intense autumn flash floods (62.7%) concentrated in western and southern districts. Conversely, severe WD transitions emerge as high-magnitude summer agricultural flash droughts (52.5%) clustered in central and northern continental plains. Crucially, while the magnitudes of these events demonstrate historical temporal stationarity, their decadal frequency doubled in the 2020s. This increase validates the idea that global warming accelerates systemic climate extremes, necessitating an urgent shift toward proactive, highly localized adaptation strategies. Full article

In regions where severe topographic constraints coincide with intensive human activity, the mechanisms underlying landscape ecological risk (LER) and its spatial differentiation remain poorly understood. In particular, the nonlinear responses and threshold effects arising from the combined influence of complex natural gradients, urban expansion, and policy interventions have not been adequately characterized, limiting effective regional ecological management and policy formulation. Taking the Taiyuan section of the Fen River Basin as the study area, this study constructed an LER index using land-use data for 2014, 2019, and 2024. Landscape metrics and spatial autocorrelation analyses were used to characterize the spatiotemporal evolution of LER, and a LightGBM-SHAP model with spatial block cross-validation was employed to identify the nonlinear effects of natural and socioeconomic drivers. A four-quadrant zoning framework integrating current risk state and driver sensitivity was then developed. The results showed that: (1) LER followed a fluctuating trajectory, rising from 2014 to 2019 and declining from 2019 to 2024, with evident spatial differentiation. Low- and relatively low-risk zones dominated about 71% of the area, while medium- to high-risk zones clustered mainly in the northeast, south, and parts of the northwest; high-risk agglomerations gradually contracted. (2) LER was driven by both natural and socioeconomic factors, with natural factors playing the stronger role. Slope, NDVI, elevation, and GDP were the key drivers. (3) The effects of these drivers were strongly nonlinear: slope increased risk at 5–13° but reduced it above 13°; NDVI displayed an inverted U-shaped relationship, with the strongest positive contribution at 0.60–0.75.; and elevation shifted from a positive to negative contribution near 1200 m. Based on these results, the framework coupling risk state and driver sensitivity delineated differentiated management units, providing fine-scale spatial guidance for ecological protection, restoration, and development control in topographically constrained river-valley basins. Full article

The effectiveness of machine learning and deep learning methods for network anomaly detection depends strongly on the quality and representativeness of the datasets used for training and evaluation. Despite recent advances, many publicly available benchmarks rely on synthetic traffic, outdated attack scenarios, or limited representation of encrypted communications. This work presents a network traffic dataset derived from operational firewall logs collected in a heterogeneous institutional environment dominated by HTTPS/TLS traffic. A structured data-centric pipeline was implemented, including preprocessing, behavioral feature engineering, unsupervised pseudo-labeling through the EFMS–KMeans algorithm, class balancing using SMOTE, and the generation of model-oriented sequential representations for deep learning analysis. The resulting dataset contains large-scale flow-level records describing volumetric, behavioral, and temporal traffic characteristics while preserving privacy through anonymization procedures. Technical validation was conducted using statistical analysis, entropy-based measurements, clustering quality metrics, and dimensionality reduction techniques, confirming data consistency, structural diversity, and class separability. The dataset is publicly available through the Mendeley Data repository together with metadata and documentation supporting anomaly detection research, encrypted traffic analysis, and the evaluation of machine learning and deep learning approaches in realistic cybersecurity environments. Full article

Emission inventories for high-altitude Andean cities rely on sea-level certification cycles that misrepresent real-world combustion conditions. This study derives altitude-resolved emission factors (EFs) for light-duty gasoline vehicles across 0–4000 m a.s.l. in Ecuador using the purpose-built Andean Ecuador Driving Cycle (aedc), validated against naturalistic data via the Percentile Speed Vector metric (PSV_m10; IGS $=1.89$ vs. IGS $=2.30$ for the WLTC). Ten vehicles (Euro III–V) were instrumented with OBD-II and portable analysers recording $\mathrm{CO}$, $\mathrm{NO}$, $\mathrm{HC}$, and ${\mathrm{CO}}_2$ at 1 Hz over a four-year campaign (2021–2025; ≈2000 h). K-Means clustering on PSV_m10 identified five operating regimes (silhouette ≈0.384). Under dynamically equivalent aedc conditions, $\mathrm{NO}$, $\mathrm{CO}$, and $\mathrm{HC}$ all peaked in the 1000–2000 m band ($\mathrm{NO}$: $0.188\mathrm{g}{\mathrm{km}}^{-1}$, $6.7\times$ the sea-level value; $\mathrm{CO}$: $4.47\mathrm{g}{\mathrm{km}}^{-1}$, $+50\%$; $\mathrm{HC}$: $0.047\mathrm{g}{\mathrm{km}}^{-1}$, $+292\%$), fell in the 2000–3000 m band, and partially rebounded above 3000 m ($\mathrm{NO}$: $0.186\mathrm{g}{\mathrm{km}}^{-1}$); ${\mathrm{CO}}_2$ instead declined monotonically with altitude (182 to $119\mathrm{g}{\mathrm{km}}^{-1}$, $-35\%$), tracking a near-stable-to-slightly-declining fuel consumption (8.56 to $8.11\mathrm{L}/100\mathrm{km}$) consistent with reduced aerodynamic drag at altitude partially offsetting the density penalty. These results show that altitude affects pollutants through distinct, non-monotonic mechanisms rather than a uniform trend, so that single-coefficient altitude corrections introduce systematic bias in Andean emission inventories. Full article

Background: Ischemic stroke (IS) became the most common type of stroke in 2021, leading to an extreme health burden globally, and has the potential to further increase the burden in the young. However, current therapies for IS are limited, and its mechanism remains unclear. Methods: SPF C57BL/6J male mice were used to establish the middle cerebral artery occlusion (MCAO) model. The Longa’s Neurological Deficit Score and TTC staining served as evaluative metrics for the efficacy of the model. Untargeted metabolomics analysis was performed based on LC-MS technology, and PCA, PLS-DA, and KEGG analysis were used to explore the differential metabolites. Results: Compared to the Sham group, the neurological deficit score of the MCAO group was significantly higher at 2 h and 72 h after surgery (p < 0.001). There was no infarct volume in the Sham group, while the infarct volume ratio of the MCAO group significantly increased compared with that of the Sham group. In total, 1836 metabolites were identified overall, and they were clustered into 14 classifications, with “lipids and lipid-like molecules” accounting for the most (34.45%). Compared with the Sham group, 95 metabolites were up-regulated, and 107 metabolites were down-regulated in the MCAO group; 138 of 202 were identified with HMDB numbers. A further clustering and KEGG pathway analysis showed that these 138 metabolites may be involved in lipid metabolism, amino acid metabolism, and nucleotide metabolism. However, FDR was not used as extracting criteria to screen out the differential metabolites, which indicated that the outcomes should be considered for further validation. Conclusions: This discovery-stage untargeted metabolomics analysis suggests that ischemic stroke may be associated with alterations in lipid, amino acid, and nucleotide metabolism in the mouse brain, but these exploratory findings require validation in larger independent cohorts using targeted metabolomics. Full article

Background/Objectives: Accurate size-matching between donor and recipient is central to adult heart transplantation, yet the optimal operational metric remains unsettled. We compared the predicted heart mass ratio (PHMr) with a ratio pairing measured donor left ventricular mass and predicted recipient left ventricular mass (mLVM:pLVM) to determine how each classifies the size acceptability of donor offers. Methods: In a retrospective single-center cohort of 594 adult donor offers evaluated for 22 recipients during 2023, offers were classified as acceptable under bilateral windows (PHMr 0.86 to 1.14, mLVM:pLVM 0.80 to 1.20) and under lower-bound-only thresholds. Recipient-clustered models with cluster-robust standard errors were the primary analysis, with a generalized estimating equation and a linear mixed model as sensitivity analyses. Prespecified subgroups were defined by sex and body mass index. Results: Under bilateral windows, PHMr classified more offers as acceptable than mLVM:pLVM (46.3% versus 40.1%), but the overall difference was not significant in the primary analysis (cluster-robust risk difference 6.2 percentage points, 95% CI −0.5 to 13.0, p = 0.068) and reached significance only in sensitivity analyses. Agreement was low (Cohen’s κ 0.12 to 0.24). The between-method difference varied by donor sex, confirmed by interaction testing: PHMr classified more male donor offers as acceptable, whereas mLVM:pLVM classified more female donor offers as acceptable. Under lower-bound-only thresholds, significant differences were confined to donor sex. Conclusions: Metric and threshold choice changed which offers were classified as acceptable. These differences reflect operational discordance rather than clinical superiority and warrant validation linked to outcomes. Full article